##################################
# Loading R libraries
##################################
library(DALEX)
library(caret)
library(randomForest)
library(e1071)
library(gbm)
library(skimr)
library(corrplot)
library(lares)
library(dplyr)
library(minerva)
library(CORElearn)
library(patchwork)
library(lime)
library(DALEXtra)
##################################
# Loading source and
# formulating the analysis set
##################################
LED <- read.csv("Life_Expectancy_Data.csv",
na.strings=c("NA","NaN"," ",""),
stringsAsFactors = FALSE)
LED <- as.data.frame(LED)
##################################
# Performing a general exploration of the data set
##################################
dim(LED)## [1] 394 23
str(LED)## 'data.frame': 394 obs. of 23 variables:
## $ COUNTRY: chr "Afghanistan" "Albania" "Algeria" "Angola" ...
## $ YEAR : int 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 ...
## $ GENDER : chr "Female" "Female" "Female" "Female" ...
## $ CONTIN : chr "Asia" "Europe" "Africa" "Africa" ...
## $ LIFEXP : num 66.4 80.2 78.1 64 78.1 ...
## $ UNEMPR : num 14.06 11.32 18.63 7.84 8.26 ...
## $ INFMOR : num 42.9 7.7 18.6 44.5 5.1 ...
## $ GDP : num 1.88e+10 1.54e+10 1.72e+11 8.94e+10 1.69e+09 ...
## $ GNI : num 1.91e+10 1.52e+10 1.68e+11 8.19e+10 1.58e+09 ...
## $ CLTECH : num 36 80.7 99.3 49.6 100 ...
## $ PERCAP : num 494 5396 3990 2810 17377 ...
## $ RTIMOR : num 15.9 11.7 20.9 26.1 0 ...
## $ TUBINC : num 189 16 61 351 0 29 26 2.2 6.9 6 ...
## $ DPTIMM : num 66 99 91 57 95 ...
## $ HEPIMM : num 66 99 91 53 99 ...
## $ MEAIMM : num 64 95 80 51 93 ...
## $ HOSBED : num 0.432 3.052 1.8 0.8 2.581 ...
## $ SANSER : num 49 99.2 86.1 51.4 85.5 ...
## $ TUBTRT : num 91 88 86 69 72.3 ...
## $ URBPOP : num 25.8 61.2 73.2 66.2 24.5 ...
## $ RURPOP : num 74.2 38.8 26.8 33.8 75.5 ...
## $ NCOMOR : num 36.2 6 12.8 19.4 17.6 ...
## $ SUIRAT : num 3.6 2.7 1.8 2.3 0.8 ...
summary(LED)## COUNTRY YEAR GENDER CONTIN
## Length:394 Min. :2019 Length:394 Length:394
## Class :character 1st Qu.:2019 Class :character Class :character
## Mode :character Median :2019 Mode :character Mode :character
## Mean :2019
## 3rd Qu.:2019
## Max. :2019
## LIFEXP UNEMPR INFMOR GDP
## Min. :51.20 Min. : 0.071 Min. : 1.40 Min. :1.880e+08
## 1st Qu.:67.61 1st Qu.: 3.560 1st Qu.: 6.00 1st Qu.:1.130e+10
## Median :74.33 Median : 5.663 Median :15.20 Median :3.865e+10
## Mean :73.07 Mean : 7.769 Mean :21.55 Mean :4.614e+11
## 3rd Qu.:79.30 3rd Qu.: 9.842 3rd Qu.:30.66 3rd Qu.:2.450e+11
## Max. :88.10 Max. :41.153 Max. :88.80 Max. :2.140e+13
## GNI CLTECH PERCAP RTIMOR
## Min. :3.754e+08 Min. : 0.00 Min. : 228.2 Min. : 0.0
## 1st Qu.:1.111e+10 1st Qu.: 33.50 1st Qu.: 2229.9 1st Qu.: 8.2
## Median :4.005e+10 Median : 79.50 Median : 6609.5 Median :16.0
## Mean :4.814e+11 Mean : 65.66 Mean : 16682.2 Mean :17.0
## 3rd Qu.:2.450e+11 3rd Qu.:100.00 3rd Qu.: 19303.5 3rd Qu.:23.9
## Max. :2.170e+13 Max. :100.00 Max. :175813.9 Max. :64.6
## TUBINC DPTIMM HEPIMM MEAIMM
## Min. : 0.0 Min. :35.00 Min. :35.00 Min. :37.00
## 1st Qu.: 12.0 1st Qu.:85.69 1st Qu.:81.31 1st Qu.:84.85
## Median : 46.0 Median :92.00 Median :91.00 Median :92.00
## Mean :103.5 Mean :87.87 Mean :86.64 Mean :87.21
## 3rd Qu.:140.0 3rd Qu.:97.00 3rd Qu.:96.00 3rd Qu.:96.00
## Max. :654.0 Max. :99.00 Max. :99.00 Max. :99.00
## HOSBED SANSER TUBTRT URBPOP
## Min. : 0.200 Min. : 8.632 Min. : 0.00 Min. : 13.25
## 1st Qu.: 1.300 1st Qu.: 63.898 1st Qu.: 73.00 1st Qu.: 41.61
## Median : 2.570 Median : 91.144 Median : 82.00 Median : 58.76
## Mean : 2.987 Mean : 77.495 Mean : 77.68 Mean : 59.09
## 3rd Qu.: 3.746 3rd Qu.: 98.516 3rd Qu.: 88.00 3rd Qu.: 77.94
## Max. :13.710 Max. :100.000 Max. :100.00 Max. :100.00
## RURPOP NCOMOR SUIRAT
## Min. : 0.00 Min. : 4.40 Min. : 0.000
## 1st Qu.:22.06 1st Qu.:13.60 1st Qu.: 3.300
## Median :41.24 Median :19.95 Median : 6.850
## Mean :40.91 Mean :20.02 Mean : 9.572
## 3rd Qu.:58.39 3rd Qu.:24.07 3rd Qu.: 11.175
## Max. :86.75 Max. :58.40 Max. :116.000
##################################
# Transforming to appropriate data types
##################################
LED$YEAR <- factor(LED$YEAR,
levels = c("2019"))
LED$GENDER <- factor(LED$GENDER,
levels = c("Male","Female"))
LED$CONTIN <- as.factor(LED$CONTIN)
##################################
# Reducing the range of values
# for certain numeric predictors
##################################
LED$GDP <- LED$GDP/1000000000
LED$GNI <- LED$GNI/1000000000
LED$PERCAP <- LED$PERCAP/1000
##################################
# Formulating a data type assessment summary
##################################
PDA <- LED
(PDA.Summary <- data.frame(
Column.Index=c(1:length(names(PDA))),
Column.Name= names(PDA),
Column.Type=sapply(PDA, function(x) class(x)),
row.names=NULL)
)## Column.Index Column.Name Column.Type
## 1 1 COUNTRY character
## 2 2 YEAR factor
## 3 3 GENDER factor
## 4 4 CONTIN factor
## 5 5 LIFEXP numeric
## 6 6 UNEMPR numeric
## 7 7 INFMOR numeric
## 8 8 GDP numeric
## 9 9 GNI numeric
## 10 10 CLTECH numeric
## 11 11 PERCAP numeric
## 12 12 RTIMOR numeric
## 13 13 TUBINC numeric
## 14 14 DPTIMM numeric
## 15 15 HEPIMM numeric
## 16 16 MEAIMM numeric
## 17 17 HOSBED numeric
## 18 18 SANSER numeric
## 19 19 TUBTRT numeric
## 20 20 URBPOP numeric
## 21 21 RURPOP numeric
## 22 22 NCOMOR numeric
## 23 23 SUIRAT numeric
##################################
# Loading dataset
##################################
DQA <- LED
##################################
# Formulating an overall data quality assessment summary
##################################
(DQA.Summary <- data.frame(
Column.Index=c(1:length(names(DQA))),
Column.Name= names(DQA),
Column.Type=sapply(DQA, function(x) class(x)),
Row.Count=sapply(DQA, function(x) nrow(DQA)),
NA.Count=sapply(DQA,function(x)sum(is.na(x))),
Fill.Rate=sapply(DQA,function(x)format(round((sum(!is.na(x))/nrow(DQA)),3),nsmall=3)),
row.names=NULL)
)## Column.Index Column.Name Column.Type Row.Count NA.Count Fill.Rate
## 1 1 COUNTRY character 394 0 1.000
## 2 2 YEAR factor 394 0 1.000
## 3 3 GENDER factor 394 0 1.000
## 4 4 CONTIN factor 394 0 1.000
## 5 5 LIFEXP numeric 394 0 1.000
## 6 6 UNEMPR numeric 394 0 1.000
## 7 7 INFMOR numeric 394 0 1.000
## 8 8 GDP numeric 394 0 1.000
## 9 9 GNI numeric 394 0 1.000
## 10 10 CLTECH numeric 394 0 1.000
## 11 11 PERCAP numeric 394 0 1.000
## 12 12 RTIMOR numeric 394 0 1.000
## 13 13 TUBINC numeric 394 0 1.000
## 14 14 DPTIMM numeric 394 0 1.000
## 15 15 HEPIMM numeric 394 0 1.000
## 16 16 MEAIMM numeric 394 0 1.000
## 17 17 HOSBED numeric 394 0 1.000
## 18 18 SANSER numeric 394 0 1.000
## 19 19 TUBTRT numeric 394 0 1.000
## 20 20 URBPOP numeric 394 0 1.000
## 21 21 RURPOP numeric 394 0 1.000
## 22 22 NCOMOR numeric 394 0 1.000
## 23 23 SUIRAT numeric 394 0 1.000
##################################
# Listing all Predictors
##################################
DQA.Predictors <- DQA[,!names(DQA) %in% c("COUNTRY","YEAR","LIFEXP")]
##################################
# Listing all numeric Predictors
##################################
DQA.Predictors.Numeric <- DQA.Predictors[,sapply(DQA.Predictors, is.numeric), drop = FALSE]
if (length(names(DQA.Predictors.Numeric))>0) {
print(paste0("There is (are) ",
(length(names(DQA.Predictors.Numeric))),
" numeric descriptor variable(s)."))
} else {
print("There are no numeric descriptor variables.")
}## [1] "There is (are) 18 numeric descriptor variable(s)."
##################################
# Listing all factor Predictors
##################################
DQA.Predictors.Factor <- DQA.Predictors[,sapply(DQA.Predictors, is.factor), drop = FALSE]
if (length(names(DQA.Predictors.Factor))>0) {
print(paste0("There is (are) ",
(length(names(DQA.Predictors.Factor))),
" factor descriptor variable(s)."))
} else {
print("There are no factor descriptor variables.")
}## [1] "There is (are) 2 factor descriptor variable(s)."
##################################
# Formulating a data quality assessment summary for factor Predictors
##################################
if (length(names(DQA.Predictors.Factor))>0) {
##################################
# Formulating a function to determine the first mode
##################################
FirstModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
ux[tab == max(tab)]
}
##################################
# Formulating a function to determine the second mode
##################################
SecondModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
fm = ux[tab == max(tab)]
sm = x[!(x %in% fm)]
usm <- unique(sm)
tabsm <- tabulate(match(sm, usm))
ifelse(is.na(usm[tabsm == max(tabsm)])==TRUE,
return("x"),
return(usm[tabsm == max(tabsm)]))
}
(DQA.Predictors.Factor.Summary <- data.frame(
Column.Name= names(DQA.Predictors.Factor),
Column.Type=sapply(DQA.Predictors.Factor, function(x) class(x)),
Unique.Count=sapply(DQA.Predictors.Factor, function(x) length(unique(x))),
First.Mode.Value=sapply(DQA.Predictors.Factor, function(x) as.character(FirstModes(x)[1])),
Second.Mode.Value=sapply(DQA.Predictors.Factor, function(x) as.character(SecondModes(x)[1])),
First.Mode.Count=sapply(DQA.Predictors.Factor, function(x) sum(na.omit(x) == FirstModes(x)[1])),
Second.Mode.Count=sapply(DQA.Predictors.Factor, function(x) sum(na.omit(x) == SecondModes(x)[1])),
Unique.Count.Ratio=sapply(DQA.Predictors.Factor, function(x) format(round((length(unique(x))/nrow(DQA.Predictors.Factor)),3), nsmall=3)),
First.Second.Mode.Ratio=sapply(DQA.Predictors.Factor, function(x) format(round((sum(na.omit(x) == FirstModes(x)[1])/sum(na.omit(x) == SecondModes(x)[1])),3), nsmall=3)),
row.names=NULL)
)
}## Column.Name Column.Type Unique.Count First.Mode.Value Second.Mode.Value
## 1 GENDER factor 2 Female x
## 2 CONTIN factor 6 Africa Asia
## First.Mode.Count Second.Mode.Count Unique.Count.Ratio First.Second.Mode.Ratio
## 1 197 0 0.005 Inf
## 2 106 100 0.015 1.060
##################################
# Formulating a data quality assessment summary for numeric Predictors
##################################
if (length(names(DQA.Predictors.Numeric))>0) {
##################################
# Formulating a function to determine the first mode
##################################
FirstModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
ux[tab == max(tab)]
}
##################################
# Formulating a function to determine the second mode
##################################
SecondModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
fm = ux[tab == max(tab)]
sm = na.omit(x)[!(na.omit(x) %in% fm)]
usm <- unique(sm)
tabsm <- tabulate(match(sm, usm))
ifelse(is.na(usm[tabsm == max(tabsm)])==TRUE,
return(0.00001),
return(usm[tabsm == max(tabsm)]))
}
(DQA.Predictors.Numeric.Summary <- data.frame(
Column.Name= names(DQA.Predictors.Numeric),
Column.Type=sapply(DQA.Predictors.Numeric, function(x) class(x)),
Unique.Count=sapply(DQA.Predictors.Numeric, function(x) length(unique(x))),
Unique.Count.Ratio=sapply(DQA.Predictors.Numeric, function(x) format(round((length(unique(x))/nrow(DQA.Predictors.Numeric)),3), nsmall=3)),
First.Mode.Value=sapply(DQA.Predictors.Numeric, function(x) format(round((FirstModes(x)[1]),3),nsmall=3)),
Second.Mode.Value=sapply(DQA.Predictors.Numeric, function(x) format(round((SecondModes(x)[1]),3),nsmall=3)),
First.Mode.Count=sapply(DQA.Predictors.Numeric, function(x) sum(na.omit(x) == FirstModes(x)[1])),
Second.Mode.Count=sapply(DQA.Predictors.Numeric, function(x) sum(na.omit(x) == SecondModes(x)[1])),
First.Second.Mode.Ratio=sapply(DQA.Predictors.Numeric, function(x) format(round((sum(na.omit(x) == FirstModes(x)[1])/sum(na.omit(x) == SecondModes(x)[1])),3), nsmall=3)),
Minimum=sapply(DQA.Predictors.Numeric, function(x) format(round(min(x,na.rm = TRUE),3), nsmall=3)),
Mean=sapply(DQA.Predictors.Numeric, function(x) format(round(mean(x,na.rm = TRUE),3), nsmall=3)),
Median=sapply(DQA.Predictors.Numeric, function(x) format(round(median(x,na.rm = TRUE),3), nsmall=3)),
Maximum=sapply(DQA.Predictors.Numeric, function(x) format(round(max(x,na.rm = TRUE),3), nsmall=3)),
Skewness=sapply(DQA.Predictors.Numeric, function(x) format(round(skewness(x,na.rm = TRUE),3), nsmall=3)),
Kurtosis=sapply(DQA.Predictors.Numeric, function(x) format(round(kurtosis(x,na.rm = TRUE),3), nsmall=3)),
Percentile25th=sapply(DQA.Predictors.Numeric, function(x) format(round(quantile(x,probs=0.25,na.rm = TRUE),3), nsmall=3)),
Percentile75th=sapply(DQA.Predictors.Numeric, function(x) format(round(quantile(x,probs=0.75,na.rm = TRUE),3), nsmall=3)),
row.names=NULL)
)
}## Column.Name Column.Type Unique.Count Unique.Count.Ratio First.Mode.Value
## 1 UNEMPR numeric 370 0.939 8.256
## 2 INFMOR numeric 245 0.622 30.235
## 3 GDP numeric 254 0.645 303.000
## 4 GNI numeric 253 0.642 2040.000
## 5 CLTECH numeric 112 0.284 100.000
## 6 PERCAP numeric 196 0.497 12.669
## 7 RTIMOR numeric 141 0.358 18.229
## 8 TUBINC numeric 146 0.371 136.043
## 9 DPTIMM numeric 45 0.114 99.000
## 10 HEPIMM numeric 45 0.114 81.308
## 11 MEAIMM numeric 48 0.122 99.000
## 12 HOSBED numeric 173 0.439 2.986
## 13 SANSER numeric 186 0.472 100.000
## 14 TUBTRT numeric 59 0.150 84.000
## 15 URBPOP numeric 191 0.485 100.000
## 16 RURPOP numeric 191 0.485 0.000
## 17 NCOMOR numeric 214 0.543 22.100
## 18 SUIRAT numeric 176 0.447 10.619
## Second.Mode.Value First.Mode.Count Second.Mode.Count First.Second.Mode.Ratio
## 1 3.924 22 2 11.000
## 2 2.100 28 7 4.000
## 3 279.000 4 3 1.333
## 4 316.000 8 4 2.000
## 5 60.593 108 34 3.176
## 6 0.494 4 2 2.000
## 7 26.800 28 6 4.667
## 8 35.000 12 10 1.200
## 9 85.685 44 30 1.467
## 10 99.000 40 38 1.053
## 11 84.855 48 30 1.600
## 12 0.400 34 8 4.250
## 13 49.006 24 2 12.000
## 14 83.000 22 20 1.100
## 15 55.985 10 4 2.500
## 16 44.015 10 4 2.500
## 17 6.800 30 5 6.000
## 18 7.600 30 8 3.750
## Minimum Mean Median Maximum Skewness Kurtosis Percentile25th
## 1 0.071 7.769 5.663 41.153 1.751 3.680 3.560
## 2 1.400 21.546 15.200 88.800 1.084 0.567 6.000
## 3 0.188 461.374 38.653 21400.000 8.619 82.715 11.304
## 4 0.375 481.441 40.048 21700.000 8.547 82.139 11.110
## 5 0.000 65.660 79.500 100.000 -0.623 -1.141 33.500
## 6 0.228 16.682 6.610 175.814 2.810 10.880 2.230
## 7 0.000 17.003 16.000 64.600 0.740 1.028 8.200
## 8 0.000 103.489 46.000 654.000 1.864 3.177 12.000
## 9 35.000 87.875 92.000 99.000 -1.856 3.434 85.685
## 10 35.000 86.640 91.000 99.000 -1.595 2.477 81.308
## 11 37.000 87.207 92.000 99.000 -1.688 2.574 84.855
## 12 0.200 2.987 2.570 13.710 1.697 3.859 1.300
## 13 8.632 77.495 91.144 100.000 -1.122 -0.155 63.898
## 14 0.000 77.675 82.000 100.000 -2.194 5.571 73.000
## 15 13.250 59.094 58.760 100.000 -0.132 -0.991 41.612
## 16 0.000 40.906 41.240 86.750 0.132 -0.991 22.058
## 17 4.400 20.021 19.950 58.400 0.864 1.551 13.600
## 18 0.000 9.572 6.850 116.000 4.082 29.352 3.300
## Percentile75th
## 1 9.842
## 2 30.659
## 3 245.000
## 4 245.000
## 5 100.000
## 6 19.304
## 7 23.900
## 8 140.000
## 9 97.000
## 10 96.000
## 11 96.000
## 12 3.746
## 13 98.516
## 14 88.000
## 15 77.942
## 16 58.388
## 17 24.075
## 18 11.175
##################################
# Identifying potential data quality issues
##################################
##################################
# Checking for missing observations
##################################
if ((nrow(DQA.Summary[DQA.Summary$NA.Count>0,]))>0){
print(paste0("Missing observations noted for ",
(nrow(DQA.Summary[DQA.Summary$NA.Count>0,])),
" variable(s) with NA.Count>0 and Fill.Rate<1.0."))
DQA.Summary[DQA.Summary$NA.Count>0,]
} else {
print("No missing observations noted.")
}## [1] "No missing observations noted."
##################################
# Checking for zero or near-zero variance Predictors
##################################
if (length(names(DQA.Predictors.Factor))==0) {
print("No factor Predictors noted.")
} else if (nrow(DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,])>0){
print(paste0("Low variance observed for ",
(nrow(DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,])),
" factor variable(s) with First.Second.Mode.Ratio>5."))
DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,]
} else {
print("No low variance factor Predictors due to high first-second mode ratio noted.")
}## [1] "Low variance observed for 1 factor variable(s) with First.Second.Mode.Ratio>5."
## Column.Name Column.Type Unique.Count First.Mode.Value Second.Mode.Value
## 1 GENDER factor 2 Female x
## First.Mode.Count Second.Mode.Count Unique.Count.Ratio First.Second.Mode.Ratio
## 1 197 0 0.005 Inf
if (length(names(DQA.Predictors.Numeric))==0) {
print("No numeric Predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,])>0){
print(paste0("Low variance observed for ",
(nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,])),
" numeric variable(s) with First.Second.Mode.Ratio>5."))
DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,]
} else {
print("No low variance numeric Predictors due to high first-second mode ratio noted.")
}## [1] "Low variance observed for 3 numeric variable(s) with First.Second.Mode.Ratio>5."
## Column.Name Column.Type Unique.Count Unique.Count.Ratio First.Mode.Value
## 1 UNEMPR numeric 370 0.939 8.256
## 13 SANSER numeric 186 0.472 100.000
## 17 NCOMOR numeric 214 0.543 22.100
## Second.Mode.Value First.Mode.Count Second.Mode.Count First.Second.Mode.Ratio
## 1 3.924 22 2 11.000
## 13 49.006 24 2 12.000
## 17 6.800 30 5 6.000
## Minimum Mean Median Maximum Skewness Kurtosis Percentile25th
## 1 0.071 7.769 5.663 41.153 1.751 3.680 3.560
## 13 8.632 77.495 91.144 100.000 -1.122 -0.155 63.898
## 17 4.400 20.021 19.950 58.400 0.864 1.551 13.600
## Percentile75th
## 1 9.842
## 13 98.516
## 17 24.075
if (length(names(DQA.Predictors.Numeric))==0) {
print("No numeric Predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Unique.Count.Ratio))<0.01,])>0){
print(paste0("Low variance observed for ",
(nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Unique.Count.Ratio))<0.01,])),
" numeric variable(s) with Unique.Count.Ratio<0.01."))
DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Unique.Count.Ratio))<0.01,]
} else {
print("No low variance numeric Predictors due to low unique count ratio noted.")
}## [1] "No low variance numeric Predictors due to low unique count ratio noted."
##################################
# Checking for skewed Predictors
##################################
if (length(names(DQA.Predictors.Numeric))==0) {
print("No numeric Predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))>3 |
as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))<(-3),])>0){
print(paste0("High skewness observed for ",
(nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))>3 |
as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))<(-3),])),
" numeric variable(s) with Skewness>3 or Skewness<(-3)."))
DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))>3 |
as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))<(-3),]
} else {
print("No skewed numeric Predictors noted.")
}## [1] "High skewness observed for 3 numeric variable(s) with Skewness>3 or Skewness<(-3)."
## Column.Name Column.Type Unique.Count Unique.Count.Ratio First.Mode.Value
## 3 GDP numeric 254 0.645 303.000
## 4 GNI numeric 253 0.642 2040.000
## 18 SUIRAT numeric 176 0.447 10.619
## Second.Mode.Value First.Mode.Count Second.Mode.Count First.Second.Mode.Ratio
## 3 279.000 4 3 1.333
## 4 316.000 8 4 2.000
## 18 7.600 30 8 3.750
## Minimum Mean Median Maximum Skewness Kurtosis Percentile25th
## 3 0.188 461.374 38.653 21400.000 8.619 82.715 11.304
## 4 0.375 481.441 40.048 21700.000 8.547 82.139 11.110
## 18 0.000 9.572 6.850 116.000 4.082 29.352 3.300
## Percentile75th
## 3 245.000
## 4 245.000
## 18 11.175
##################################
# Loading dataset
##################################
DPA <- LED
##################################
# Gathering descriptive statistics
##################################
(DPA_Skimmed <- skim(DPA))| Name | DPA |
| Number of rows | 394 |
| Number of columns | 23 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| factor | 3 |
| numeric | 19 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| COUNTRY | 0 | 1 | 4 | 30 | 0 | 197 | 0 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| YEAR | 0 | 1 | FALSE | 1 | 201: 394 |
| GENDER | 0 | 1 | FALSE | 2 | Mal: 197, Fem: 197 |
| CONTIN | 0 | 1 | FALSE | 6 | Afr: 106, Asi: 100, Eur: 86, Nor: 52 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| LIFEXP | 0 | 1 | 73.07 | 7.82 | 51.20 | 67.61 | 74.32 | 79.30 | 88.10 | ▁▃▆▇▃ |
| UNEMPR | 0 | 1 | 7.77 | 6.35 | 0.07 | 3.56 | 5.66 | 9.84 | 41.15 | ▇▂▁▁▁ |
| INFMOR | 0 | 1 | 21.55 | 18.67 | 1.40 | 6.00 | 15.20 | 30.66 | 88.80 | ▇▃▂▁▁ |
| GDP | 0 | 1 | 461.37 | 1920.36 | 0.19 | 11.30 | 38.65 | 245.00 | 21400.00 | ▇▁▁▁▁ |
| GNI | 0 | 1 | 481.44 | 1942.86 | 0.38 | 11.11 | 40.05 | 245.00 | 21700.00 | ▇▁▁▁▁ |
| CLTECH | 0 | 1 | 65.66 | 36.33 | 0.00 | 33.50 | 79.50 | 100.00 | 100.00 | ▃▁▁▂▇ |
| PERCAP | 0 | 1 | 16.68 | 24.32 | 0.23 | 2.23 | 6.61 | 19.30 | 175.81 | ▇▁▁▁▁ |
| RTIMOR | 0 | 1 | 17.00 | 10.34 | 0.00 | 8.20 | 16.00 | 23.90 | 64.60 | ▇▇▅▁▁ |
| TUBINC | 0 | 1 | 103.49 | 133.68 | 0.00 | 12.00 | 46.00 | 140.00 | 654.00 | ▇▂▁▁▁ |
| DPTIMM | 0 | 1 | 87.87 | 12.41 | 35.00 | 85.69 | 92.00 | 97.00 | 99.00 | ▁▁▁▃▇ |
| HEPIMM | 0 | 1 | 86.64 | 12.72 | 35.00 | 81.31 | 91.00 | 96.00 | 99.00 | ▁▁▁▃▇ |
| MEAIMM | 0 | 1 | 87.21 | 13.17 | 37.00 | 84.85 | 92.00 | 96.00 | 99.00 | ▁▁▁▃▇ |
| HOSBED | 0 | 1 | 2.99 | 2.35 | 0.20 | 1.30 | 2.57 | 3.75 | 13.71 | ▇▅▂▁▁ |
| SANSER | 0 | 1 | 77.49 | 27.63 | 8.63 | 63.90 | 91.14 | 98.52 | 100.00 | ▁▁▁▂▇ |
| TUBTRT | 0 | 1 | 77.68 | 16.97 | 0.00 | 73.00 | 82.00 | 88.00 | 100.00 | ▁▁▁▅▇ |
| URBPOP | 0 | 1 | 59.09 | 23.24 | 13.25 | 41.61 | 58.76 | 77.94 | 100.00 | ▅▆▇▇▆ |
| RURPOP | 0 | 1 | 40.91 | 23.24 | 0.00 | 22.06 | 41.24 | 58.39 | 86.75 | ▆▇▇▆▅ |
| NCOMOR | 0 | 1 | 20.02 | 8.40 | 4.40 | 13.60 | 19.95 | 24.08 | 58.40 | ▅▇▂▁▁ |
| SUIRAT | 0 | 1 | 9.57 | 10.49 | 0.00 | 3.30 | 6.85 | 11.17 | 116.00 | ▇▁▁▁▁ |
##################################
# Outlier Treatment
##################################
##################################
# Listing all Predictors
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("COUNTRY","YEAR","LIFEXP")]
##################################
# Listing all numeric predictors
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,sapply(DPA.Predictors, is.numeric)]
##################################
# Identifying outliers for the numeric predictors
##################################
OutlierCountList <- c()
for (i in 1:ncol(DPA.Predictors.Numeric)) {
Outliers <- boxplot.stats(DPA.Predictors.Numeric[,i])$out
OutlierCount <- length(Outliers)
OutlierCountList <- append(OutlierCountList,OutlierCount)
OutlierIndices <- which(DPA.Predictors.Numeric[,i] %in% c(Outliers))
print(
ggplot(DPA.Predictors.Numeric, aes(x=DPA.Predictors.Numeric[,i])) +
geom_boxplot() +
theme_bw() +
theme(axis.text.y=element_blank(),
axis.ticks.y=element_blank()) +
xlab(names(DPA.Predictors.Numeric)[i]) +
labs(title=names(DPA.Predictors.Numeric)[i],
subtitle=paste0(OutlierCount, " Outlier(s) Detected")))
}##################################
# Formulating the histogram
# for the numeric predictors
##################################
for (i in 1:ncol(DPA.Predictors.Numeric)) {
Median <- format(round(median(DPA.Predictors.Numeric[,i],na.rm = TRUE),2), nsmall=2)
Mean <- format(round(mean(DPA.Predictors.Numeric[,i],na.rm = TRUE),2), nsmall=2)
Skewness <- format(round(skewness(DPA.Predictors.Numeric[,i],na.rm = TRUE),2), nsmall=2)
print(
ggplot(DPA.Predictors.Numeric, aes(x=DPA.Predictors.Numeric[,i])) +
geom_histogram(binwidth=1,color="black", fill="white") +
geom_vline(aes(xintercept=mean(DPA.Predictors.Numeric[,i])),
color="blue", size=1) +
geom_vline(aes(xintercept=median(DPA.Predictors.Numeric[,i])),
color="red", size=1) +
theme_bw() +
ylab("Count") +
xlab(names(DPA.Predictors.Numeric)[i]) +
labs(title=names(DPA.Predictors.Numeric)[i],
subtitle=paste0("Median = ", Median,
", Mean = ", Mean,
", Skewness = ", Skewness)))
}##################################
# Investigating distributional anomalies
# observed for several predictors
##################################
(INFMOR_Unique <- DPA %>%
group_by(INFMOR) %>%
summarize(Distinct_INFMOR = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_INFMOR)) %>%
slice(1:5))## # A tibble: 5 x 2
## INFMOR Distinct_INFMOR
## <dbl> <int>
## 1 30.2 14
## 2 2.1 7
## 3 6.4 6
## 4 1.7 4
## 5 2.5 4
(INFMOR_Unique_Country <- DPA[round(DPA$INFMOR,digits=1)==30.2,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "New Caledonia" "Puerto Rico"
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [15] "Aruba" "Bermuda"
## [17] "Channel Islands" "Faroe Islands"
## [19] "French Polynesia" "Guam"
## [21] "Hong Kong SAR, China" "Kosovo"
## [23] "Liechtenstein" "Macao SAR, China"
## [25] "New Caledonia" "Puerto Rico"
## [27] "St. Martin (French part)" "Virgin Islands (U.S.)"
DPA %>%
group_by(CLTECH) %>%
summarize(Distinct_CLTECH = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_CLTECH)) %>%
slice(1:5)## # A tibble: 5 x 2
## CLTECH Distinct_CLTECH
## <dbl> <int>
## 1 100 54
## 2 60.6 17
## 3 9.30 3
## 4 99.9 3
## 5 0.2 2
(CLTECH_Unique_Country <- DPA[round(DPA$CLTECH,digits=1)==60.6,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Lebanon" "Libya"
## [11] "Liechtenstein" "Macao SAR, China"
## [13] "New Caledonia" "Puerto Rico"
## [15] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [17] "West Bank and Gaza" "Aruba"
## [19] "Bermuda" "Channel Islands"
## [21] "Faroe Islands" "French Polynesia"
## [23] "Guam" "Hong Kong SAR, China"
## [25] "Kosovo" "Lebanon"
## [27] "Libya" "Liechtenstein"
## [29] "Macao SAR, China" "New Caledonia"
## [31] "Puerto Rico" "St. Martin (French part)"
## [33] "Virgin Islands (U.S.)" "West Bank and Gaza"
DPA %>%
group_by(RTIMOR) %>%
summarize(Distinct_RTIMOR = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_RTIMOR)) %>%
slice(1:5)## # A tibble: 5 x 2
## RTIMOR Distinct_RTIMOR
## <dbl> <int>
## 1 18.2 14
## 2 3.9 3
## 3 5.1 3
## 4 5.3 3
## 5 12.7 3
(RTIMOR_Unique_Country <- DPA[round(DPA$RTIMOR,digits=1)==18.2,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "New Caledonia" "Puerto Rico"
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [15] "Aruba" "Bermuda"
## [17] "Channel Islands" "Faroe Islands"
## [19] "French Polynesia" "Guam"
## [21] "Hong Kong SAR, China" "Kosovo"
## [23] "Liechtenstein" "Macao SAR, China"
## [25] "New Caledonia" "Puerto Rico"
## [27] "St. Martin (French part)" "Virgin Islands (U.S.)"
DPA %>%
group_by(DPTIMM) %>%
summarize(Distinct_DPTIMM = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_DPTIMM)) %>%
slice(1:5)## # A tibble: 5 x 2
## DPTIMM Distinct_DPTIMM
## <dbl> <int>
## 1 99 22
## 2 85.7 15
## 3 97 14
## 4 98 14
## 5 95 13
(DPTIMM_Unique_Country <- DPA[round(DPA$DPTIMM,digits=1)==85.7,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "New Caledonia" "Puerto Rico"
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [15] "West Bank and Gaza" "Aruba"
## [17] "Bermuda" "Channel Islands"
## [19] "Faroe Islands" "French Polynesia"
## [21] "Guam" "Hong Kong SAR, China"
## [23] "Kosovo" "Liechtenstein"
## [25] "Macao SAR, China" "New Caledonia"
## [27] "Puerto Rico" "St. Martin (French part)"
## [29] "Virgin Islands (U.S.)" "West Bank and Gaza"
DPA %>%
group_by(HEPIMM) %>%
summarize(Distinct_HEPIMM = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_HEPIMM)) %>%
slice(1:5)## # A tibble: 5 x 2
## HEPIMM Distinct_HEPIMM
## <dbl> <int>
## 1 81.3 20
## 2 99 19
## 3 97 17
## 4 98 11
## 5 92 10
(HEPIMM_Unique_Country <- DPA[round(DPA$HEPIMM,digits=1)==81.3,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Denmark"
## [5] "Faroe Islands" "Finland"
## [7] "French Polynesia" "Guam"
## [9] "Hong Kong SAR, China" "Hungary"
## [11] "Iceland" "Kosovo"
## [13] "Liechtenstein" "Macao SAR, China"
## [15] "New Caledonia" "Puerto Rico"
## [17] "Slovenia" "St. Martin (French part)"
## [19] "Virgin Islands (U.S.)" "West Bank and Gaza"
## [21] "Aruba" "Bermuda"
## [23] "Channel Islands" "Denmark"
## [25] "Faroe Islands" "Finland"
## [27] "French Polynesia" "Guam"
## [29] "Hong Kong SAR, China" "Hungary"
## [31] "Iceland" "Kosovo"
## [33] "Liechtenstein" "Macao SAR, China"
## [35] "New Caledonia" "Puerto Rico"
## [37] "Slovenia" "St. Martin (French part)"
## [39] "Virgin Islands (U.S.)" "West Bank and Gaza"
DPA %>%
group_by(MEAIMM) %>%
summarize(Distinct_MEAIMM = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_MEAIMM)) %>%
slice(1:5)## # A tibble: 5 x 2
## MEAIMM Distinct_MEAIMM
## <dbl> <int>
## 1 99 24
## 2 84.9 15
## 3 95 14
## 4 96 14
## 5 98 13
(MEAIMM_Unique_Country <- DPA[round(DPA$MEAIMM,digits=1)==84.9,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "New Caledonia" "Puerto Rico"
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [15] "West Bank and Gaza" "Aruba"
## [17] "Bermuda" "Channel Islands"
## [19] "Faroe Islands" "French Polynesia"
## [21] "Guam" "Hong Kong SAR, China"
## [23] "Kosovo" "Liechtenstein"
## [25] "Macao SAR, China" "New Caledonia"
## [27] "Puerto Rico" "St. Martin (French part)"
## [29] "Virgin Islands (U.S.)" "West Bank and Gaza"
DPA %>%
group_by(HOSBED) %>%
summarize(Distinct_HOSBED = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_HOSBED)) %>%
slice(1:5)## # A tibble: 5 x 2
## HOSBED Distinct_HOSBED
## <dbl> <int>
## 1 2.99 17
## 2 0.4 4
## 3 0.8 2
## 4 0.85 2
## 5 0.9 2
(HOSBED_Unique_Country <- DPA[round(DPA$HOSBED,digits=1)==3.0,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "Namibia" "New Caledonia"
## [13] "Papua New Guinea" "Puerto Rico"
## [15] "South Sudan" "St. Martin (French part)"
## [17] "Virgin Islands (U.S.)" "West Bank and Gaza"
## [19] "Aruba" "Bermuda"
## [21] "Channel Islands" "Faroe Islands"
## [23] "French Polynesia" "Guam"
## [25] "Hong Kong SAR, China" "Kosovo"
## [27] "Liechtenstein" "Macao SAR, China"
## [29] "Namibia" "New Caledonia"
## [31] "Papua New Guinea" "Puerto Rico"
## [33] "South Sudan" "St. Martin (French part)"
## [35] "Virgin Islands (U.S.)" "West Bank and Gaza"
DPA %>%
group_by(NCOMOR) %>%
summarize(Distinct_NCOMOR = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_NCOMOR)) %>%
slice(1:5)## # A tibble: 5 x 2
## NCOMOR Distinct_NCOMOR
## <dbl> <int>
## 1 22.1 15
## 2 6.8 5
## 3 13.6 5
## 4 15.2 5
## 5 17.5 5
(NCOMOR_Unique_Country <- DPA[round(DPA$NCOMOR,digits=1)==22.1,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Burkina Faso" "Channel Islands"
## [5] "Faroe Islands" "French Polynesia"
## [7] "Guam" "Hong Kong SAR, China"
## [9] "Kosovo" "Liechtenstein"
## [11] "Macao SAR, China" "New Caledonia"
## [13] "Puerto Rico" "St. Martin (French part)"
## [15] "Virgin Islands (U.S.)" "West Bank and Gaza"
## [17] "Aruba" "Bermuda"
## [19] "Channel Islands" "Dominican Republic"
## [21] "Equatorial Guinea" "Estonia"
## [23] "Faroe Islands" "French Polynesia"
## [25] "Guam" "Hong Kong SAR, China"
## [27] "Kosovo" "Liechtenstein"
## [29] "Macao SAR, China" "New Caledonia"
## [31] "Puerto Rico" "Sierra Leone"
## [33] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [35] "West Bank and Gaza"
DPA %>%
group_by(SUIRAT) %>%
summarize(Distinct_SUIRAT = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_SUIRAT)) %>%
slice(1:5)## # A tibble: 5 x 2
## SUIRAT Distinct_SUIRAT
## <dbl> <int>
## 1 10.6 15
## 2 7.6 8
## 3 1.7 7
## 4 2 7
## 5 2.8 7
(SUIRAT_Unique_Country <- DPA[round(DPA$SUIRAT,digits=1)==10.6,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "New Caledonia" "Puerto Rico"
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [15] "West Bank and Gaza" "Aruba"
## [17] "Bermuda" "Channel Islands"
## [19] "Congo, Dem. Rep." "Faroe Islands"
## [21] "French Polynesia" "Guam"
## [23] "Hong Kong SAR, China" "Kosovo"
## [25] "Liechtenstein" "Macao SAR, China"
## [27] "New Caledonia" "Puerto Rico"
## [29] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [31] "West Bank and Gaza"
(AnomalousVariables_Unique_Country <- MEAIMM_Unique_Country)## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "New Caledonia" "Puerto Rico"
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [15] "West Bank and Gaza" "Aruba"
## [17] "Bermuda" "Channel Islands"
## [19] "Faroe Islands" "French Polynesia"
## [21] "Guam" "Hong Kong SAR, China"
## [23] "Kosovo" "Liechtenstein"
## [25] "Macao SAR, China" "New Caledonia"
## [27] "Puerto Rico" "St. Martin (French part)"
## [29] "Virgin Islands (U.S.)" "West Bank and Gaza"
##################################
# Removing rows with anomalous values
##################################
dim(DPA)## [1] 394 23
DPA <- DPA[!(DPA$COUNTRY %in% AnomalousVariables_Unique_Country),]
dim(DPA)## [1] 364 23
##################################
# Listing all Predictors
# for the updated data
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("COUNTRY","YEAR","LIFEXP")]
##################################
# Listing all numeric predictors
# for the updated data
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,sapply(DPA.Predictors, is.numeric)]##################################
# Zero and Near-Zero Variance
##################################
##################################
# Identifying columns with low variance
###################################
DPA_LowVariance <- nearZeroVar(DPA,
freqCut = 80/20,
uniqueCut = 10,
saveMetrics= TRUE)
(DPA_LowVariance[DPA_LowVariance$nzv,])## freqRatio percentUnique zeroVar nzv
## YEAR 0 0.2747253 TRUE TRUE
if ((nrow(DPA_LowVariance[DPA_LowVariance$nzv,]))==0){
print("No low variance predictors noted.")
} else {
print(paste0("Low variance observed for ",
(nrow(DPA_LowVariance[DPA_LowVariance$nzv,])),
" numeric variable(s) with First.Second.Mode.Ratio>4 and Unique.Count.Ratio<0.10."))
DPA_LowVarianceForRemoval <- (nrow(DPA_LowVariance[DPA_LowVariance$nzv,]))
print(paste0("Low variance can be resolved by removing ",
(nrow(DPA_LowVariance[DPA_LowVariance$nzv,])),
" numeric variable(s)."))
for (j in 1:DPA_LowVarianceForRemoval) {
DPA_LowVarianceRemovedVariable <- rownames(DPA_LowVariance[DPA_LowVariance$nzv,])[j]
print(paste0("Variable ",
j,
" for removal: ",
DPA_LowVarianceRemovedVariable))
}
DPA %>%
skim() %>%
dplyr::filter(skim_variable %in% rownames(DPA_LowVariance[DPA_LowVariance$nzv,]))
}## [1] "Low variance observed for 1 numeric variable(s) with First.Second.Mode.Ratio>4 and Unique.Count.Ratio<0.10."
## [1] "Low variance can be resolved by removing 1 numeric variable(s)."
## [1] "Variable 1 for removal: YEAR"
| Name | Piped data |
| Number of rows | 364 |
| Number of columns | 23 |
| _______________________ | |
| Column type frequency: | |
| factor | 1 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| YEAR | 0 | 1 | FALSE | 1 | 201: 364 |
##################################
# Collinearity
##################################
##################################
# Visualizing pairwise correlation between predictors
##################################
(DPA_Correlation <- cor(DPA.Predictors.Numeric,
method = "pearson",
use="pairwise.complete.obs"))## UNEMPR INFMOR GDP GNI CLTECH PERCAP
## UNEMPR 1.00000000 0.08921802 -0.09715921 -0.09686799 0.06069602 -0.18518395
## INFMOR 0.08921802 1.00000000 -0.16691246 -0.16605106 -0.77581569 -0.51823250
## GDP -0.09715921 -0.16691246 1.00000000 0.99990577 0.13752124 0.26220470
## GNI -0.09686799 -0.16605106 0.99990577 1.00000000 0.13685071 0.26229594
## CLTECH 0.06069602 -0.77581569 0.13752124 0.13685071 1.00000000 0.52998364
## PERCAP -0.18518395 -0.51823250 0.26220470 0.26229594 0.52998364 1.00000000
## RTIMOR 0.13453252 0.65132565 -0.11475473 -0.11451028 -0.59183716 -0.56088001
## TUBINC 0.16124757 0.58376872 -0.09323916 -0.09283727 -0.55243739 -0.38062151
## DPTIMM -0.12876443 -0.58547646 0.10789977 0.10739430 0.45449772 0.31093085
## HEPIMM -0.09314054 -0.51310504 0.08470102 0.08441932 0.38820759 0.19114586
## MEAIMM -0.15097736 -0.58103197 0.10108399 0.10027512 0.50455989 0.31282969
## HOSBED -0.09030568 -0.52521834 0.13340127 0.13526591 0.43901930 0.32835573
## SANSER 0.01743227 -0.82413990 0.15602727 0.15526943 0.86208147 0.48509427
## TUBTRT -0.05294186 0.30145355 -0.02991535 -0.02993892 -0.32585084 -0.38908786
## URBPOP 0.08774280 -0.53383928 0.16951798 0.16915063 0.64092796 0.57384548
## RURPOP -0.08774280 0.53383928 -0.16951798 -0.16915063 -0.64092796 -0.57384548
## NCOMOR 0.08876876 0.47793168 -0.15463824 -0.15418319 -0.47327879 -0.51434429
## SUIRAT 0.04227853 0.01383619 0.05563045 0.05591448 0.05255453 0.08998525
## RTIMOR TUBINC DPTIMM HEPIMM MEAIMM HOSBED
## UNEMPR 0.13453252 0.16124757 -0.1287644 -0.09314054 -0.15097736 -0.09030568
## INFMOR 0.65132565 0.58376872 -0.5854765 -0.51310504 -0.58103197 -0.52521834
## GDP -0.11475473 -0.09323916 0.1078998 0.08470102 0.10108399 0.13340127
## GNI -0.11451028 -0.09283727 0.1073943 0.08441932 0.10027512 0.13526591
## CLTECH -0.59183716 -0.55243739 0.4544977 0.38820759 0.50455989 0.43901930
## PERCAP -0.56088001 -0.38062151 0.3109309 0.19114586 0.31282969 0.32835573
## RTIMOR 1.00000000 0.41804642 -0.3375163 -0.26020225 -0.30876424 -0.49070005
## TUBINC 0.41804642 1.00000000 -0.3789808 -0.32542031 -0.38118795 -0.19831333
## DPTIMM -0.33751628 -0.37898079 1.0000000 0.95025399 0.88174261 0.32804245
## HEPIMM -0.26020225 -0.32542031 0.9502540 1.00000000 0.86420155 0.27828765
## MEAIMM -0.30876424 -0.38118795 0.8817426 0.86420155 1.00000000 0.33921761
## HOSBED -0.49070005 -0.19831333 0.3280425 0.27828765 0.33921761 1.00000000
## SANSER -0.65108358 -0.55657212 0.4749098 0.41097807 0.52634353 0.48298288
## TUBTRT 0.32106450 0.23187021 -0.1508346 -0.10718796 -0.15128872 -0.20100265
## URBPOP -0.39930232 -0.33604248 0.2477115 0.16791625 0.27392265 0.29499246
## RURPOP 0.39930232 0.33604248 -0.2477115 -0.16791625 -0.27392265 -0.29499246
## NCOMOR 0.27623132 0.44177318 -0.2463052 -0.18345234 -0.25685607 -0.14297975
## SUIRAT -0.08928389 0.13461778 0.1016538 0.07309085 0.07173647 0.23601976
## SANSER TUBTRT URBPOP RURPOP NCOMOR SUIRAT
## UNEMPR 0.01743227 -0.05294186 0.08774280 -0.08774280 0.08876876 0.04227853
## INFMOR -0.82413990 0.30145355 -0.53383928 0.53383928 0.47793168 0.01383619
## GDP 0.15602727 -0.02991535 0.16951798 -0.16951798 -0.15463824 0.05563045
## GNI 0.15526943 -0.02993892 0.16915063 -0.16915063 -0.15418319 0.05591448
## CLTECH 0.86208147 -0.32585084 0.64092796 -0.64092796 -0.47327879 0.05255453
## PERCAP 0.48509427 -0.38908786 0.57384548 -0.57384548 -0.51434429 0.08998525
## RTIMOR -0.65108358 0.32106450 -0.39930232 0.39930232 0.27623132 -0.08928389
## TUBINC -0.55657212 0.23187021 -0.33604248 0.33604248 0.44177318 0.13461778
## DPTIMM 0.47490980 -0.15083460 0.24771148 -0.24771148 -0.24630524 0.10165384
## HEPIMM 0.41097807 -0.10718796 0.16791625 -0.16791625 -0.18345234 0.07309085
## MEAIMM 0.52634353 -0.15128872 0.27392265 -0.27392265 -0.25685607 0.07173647
## HOSBED 0.48298288 -0.20100265 0.29499246 -0.29499246 -0.14297975 0.23601976
## SANSER 1.00000000 -0.29773553 0.57044364 -0.57044364 -0.38904255 0.06984751
## TUBTRT -0.29773553 1.00000000 -0.31075615 0.31075615 0.24910749 -0.03195240
## URBPOP 0.57044364 -0.31075615 1.00000000 -1.00000000 -0.49977042 0.00916115
## RURPOP -0.57044364 0.31075615 -1.00000000 1.00000000 0.49977042 -0.00916115
## NCOMOR -0.38904255 0.24910749 -0.49977042 0.49977042 1.00000000 0.43953077
## SUIRAT 0.06984751 -0.03195240 0.00916115 -0.00916115 0.43953077 1.00000000
DPA_CorrelationTest <- cor.mtest(DPA.Predictors.Numeric,
method = "pearson",
conf.level = 0.95)
corrplot(cor(DPA.Predictors.Numeric,
method = "pearson",
use="pairwise.complete.obs"),
method = "circle",
type = "upper",
order = "original",
tl.col = "black",
tl.cex = 0.75,
tl.srt = 90,
sig.level = 0.05,
p.mat = DPA_CorrelationTest$p,
insig = "blank")corrplot(cor(DPA.Predictors.Numeric,
method = "pearson",
use="pairwise.complete.obs"),
method = "number",
type = "upper",
order = "original",
tl.col = "black",
tl.cex = 0.75,
tl.srt = 90,
sig.level = 0.05,
p.mat = DPA_CorrelationTest$p,
insig = "blank")##################################
# Identifying the highly correlated variables
##################################
DPA_Correlation <- cor(DPA.Predictors.Numeric,
method = "pearson",
use="pairwise.complete.obs")
(DPA_HighlyCorrelatedCount <- sum(abs(DPA_Correlation[upper.tri(DPA_Correlation)])>0.80))## [1] 7
if (DPA_HighlyCorrelatedCount > 0) {
DPA_HighlyCorrelated <- findCorrelation(DPA_Correlation, cutoff = 0.80)
(DPA_HighlyCorrelatedForRemoval <- length(DPA_HighlyCorrelated))
print(paste0("High correlation can be resolved by removing ",
(DPA_HighlyCorrelatedForRemoval),
" numeric variable(s)."))
for (j in 1:DPA_HighlyCorrelatedForRemoval) {
DPA_HighlyCorrelatedRemovedVariable <- colnames(DPA.Predictors.Numeric)[DPA_HighlyCorrelated[j]]
print(paste0("Variable ",
j,
" for removal: ",
DPA_HighlyCorrelatedRemovedVariable))
}
}## [1] "High correlation can be resolved by removing 6 numeric variable(s)."
## [1] "Variable 1 for removal: INFMOR"
## [1] "Variable 2 for removal: CLTECH"
## [1] "Variable 3 for removal: URBPOP"
## [1] "Variable 4 for removal: MEAIMM"
## [1] "Variable 5 for removal: DPTIMM"
## [1] "Variable 6 for removal: GNI"
##################################
# Linear Dependencies
##################################
##################################
# Finding linear dependencies
##################################
DPA_LinearlyDependent <- findLinearCombos(DPA.Predictors.Numeric)
##################################
# Identifying the linearly dependent variables
##################################
DPA_LinearlyDependent <- findLinearCombos(DPA.Predictors.Numeric)
(DPA_LinearlyDependentCount <- length(DPA_LinearlyDependent$linearCombos))## [1] 0
if (DPA_LinearlyDependentCount == 0) {
print("No linearly dependent predictors noted.")
} else {
print(paste0("Linear dependency observed for ",
(DPA_LinearlyDependentCount),
" subset(s) of numeric variable(s)."))
for (i in 1:DPA_LinearlyDependentCount) {
DPA_LinearlyDependentSubset <- colnames(DPA.Predictors.Numeric)[DPA_LinearlyDependent$linearCombos[[i]]]
print(paste0("Linear dependent variable(s) for subset ",
i,
" include: ",
DPA_LinearlyDependentSubset))
}
}## [1] "No linearly dependent predictors noted."
##################################
# Identifying the linearly dependent variables for removal
##################################
if (DPA_LinearlyDependentCount > 0) {
DPA_LinearlyDependent <- findLinearCombos(DPA.Predictors.Numeric)
DPA_LinearlyDependentForRemoval <- length(DPA_LinearlyDependent$remove)
print(paste0("Linear dependency can be resolved by removing ",
(DPA_LinearlyDependentForRemoval),
" numeric variable(s)."))
for (j in 1:DPA_LinearlyDependentForRemoval) {
DPA_LinearlyDependentRemovedVariable <- colnames(DPA.Predictors.Numeric)[DPA_LinearlyDependent$remove[j]]
print(paste0("Variable ",
j,
" for removal: ",
DPA_LinearlyDependentRemovedVariable))
}
}##################################
# Shape Transformation
##################################
##################################
# Applying a Box-Cox transformation
##################################
DPA_BoxCox <- preProcess(DPA.Predictors.Numeric, method = c("BoxCox"))
DPA_BoxCoxTransformed <- predict(DPA_BoxCox, DPA.Predictors.Numeric)
for (i in 1:ncol(DPA_BoxCoxTransformed)) {
Median <- format(round(median(DPA_BoxCoxTransformed[,i],na.rm = TRUE),2), nsmall=2)
Mean <- format(round(mean(DPA_BoxCoxTransformed[,i],na.rm = TRUE),2), nsmall=2)
Skewness <- format(round(skewness(DPA_BoxCoxTransformed[,i],na.rm = TRUE),2), nsmall=2)
print(
ggplot(DPA_BoxCoxTransformed, aes(x=DPA_BoxCoxTransformed[,i])) +
geom_histogram(binwidth=1,color="black", fill="white") +
geom_vline(aes(xintercept=mean(DPA_BoxCoxTransformed[,i])),
color="blue", size=1) +
geom_vline(aes(xintercept=median(DPA_BoxCoxTransformed[,i])),
color="red", size=1) +
theme_bw() +
ylab("Count") +
xlab(names(DPA_BoxCoxTransformed)[i]) +
labs(title=names(DPA_BoxCoxTransformed)[i],
subtitle=paste0("Median = ", Median,
", Mean = ", Mean,
", Skewness = ", Skewness)))
}##################################
# Identifying outliers for the numeric predictors
##################################
OutlierCountList <- c()
for (i in 1:ncol(DPA_BoxCoxTransformed)) {
Outliers <- boxplot.stats(DPA_BoxCoxTransformed[,i])$out
OutlierCount <- length(Outliers)
OutlierCountList <- append(OutlierCountList,OutlierCount)
OutlierIndices <- which(DPA_BoxCoxTransformed[,i] %in% c(Outliers))
print(
ggplot(DPA_BoxCoxTransformed, aes(x=DPA_BoxCoxTransformed[,i])) +
geom_boxplot() +
theme_bw() +
theme(axis.text.y=element_blank(),
axis.ticks.y=element_blank()) +
xlab(names(DPA_BoxCoxTransformed)[i]) +
labs(title=names(DPA_BoxCoxTransformed)[i],
subtitle=paste0(OutlierCount, " Outlier(s) Detected")))
}DPA_BoxCoxTransformed <- cbind(DPA_BoxCoxTransformed,DPA[,c("COUNTRY",
"YEAR",
"GENDER",
"CONTIN",
"LIFEXP")])##################################
# Creating the pre-modelling
# train set
##################################
PMA <- DPA_BoxCoxTransformed[,!names(DPA_BoxCoxTransformed) %in% c("YEAR",
"GNI",
"DPTIMM",
"MEAIMM",
"URBPOP",
"SANSER",
"TUBINC",
"TUBTRT",
"SUIRAT")]
##################################
# Gathering descriptive statistics
##################################
(PMA_Skimmed <- skim(PMA))| Name | PMA |
| Number of rows | 364 |
| Number of columns | 14 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| factor | 2 |
| numeric | 11 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| COUNTRY | 0 | 1 | 4 | 30 | 0 | 182 | 0 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| GENDER | 0 | 1 | FALSE | 2 | Mal: 182, Fem: 182 |
| CONTIN | 0 | 1 | FALSE | 6 | Afr: 106, Asi: 94, Eur: 78, Nor: 42 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| UNEMPR | 0 | 1 | 2.13 | 1.21 | -2.05 | 1.42 | 2.00 | 2.90 | 5.26 | ▁▂▇▅▂ |
| INFMOR | 0 | 1 | 2.56 | 1.06 | 0.34 | 1.70 | 2.63 | 3.49 | 4.49 | ▅▆▇▇▆ |
| GDP | 0 | 1 | 3.85 | 2.21 | -1.67 | 2.55 | 3.76 | 5.53 | 9.97 | ▂▇▇▆▁ |
| CLTECH | 0 | 1 | 66.08 | 37.77 | 0.00 | 30.30 | 83.95 | 100.00 | 100.00 | ▃▁▁▂▇ |
| PERCAP | 0 | 1 | 1.75 | 1.40 | -1.48 | 0.65 | 1.80 | 2.82 | 4.73 | ▂▆▇▆▃ |
| RTIMOR | 0 | 1 | 16.97 | 10.72 | 0.00 | 8.00 | 15.30 | 25.60 | 64.60 | ▇▆▅▁▁ |
| HEPIMM | 0 | 1 | 3877.07 | 1006.71 | 612.00 | 3304.98 | 4231.50 | 4704.00 | 4900.00 | ▁▁▁▃▇ |
| HOSBED | 0 | 1 | 0.76 | 0.86 | -1.61 | 0.14 | 0.83 | 1.40 | 2.62 | ▁▅▇▇▃ |
| RURPOP | 0 | 1 | 41.73 | 22.68 | 0.00 | 22.62 | 41.64 | 59.76 | 86.75 | ▅▇▇▆▅ |
| NCOMOR | 0 | 1 | 4.67 | 1.08 | 1.87 | 3.93 | 4.72 | 5.38 | 7.96 | ▂▅▇▃▁ |
| LIFEXP | 0 | 1 | 72.50 | 7.77 | 51.20 | 66.93 | 73.53 | 78.54 | 87.45 | ▁▃▆▇▅ |
##################################
# Loading dataset
##################################
PME <- PMA
PME.Numeric <- PME[,sapply(PME, is.numeric), drop = FALSE]
##################################
# Listing all Predictors
##################################
PME.Predictors <- PME[,!names(PME) %in% c("COUNTRY","LIFEXP")]
##################################
# Listing all numeric Predictors
##################################
PME.Predictors.Numeric <- PME.Predictors[,sapply(PME.Predictors, is.numeric), drop = FALSE]
ncol(PME.Predictors.Numeric)## [1] 10
##################################
# Listing all numeric Predictors
##################################
PME.Predictors.Factor <- PME.Predictors[,sapply(PME.Predictors, is.factor), drop = FALSE]
ncol(PME.Predictors.Factor)## [1] 2
##################################
# Formulating the scatter plot
##################################
featurePlot(x = PME.Predictors.Numeric,
y = PME$LIFEXP,
plot = "scatter",
type = c("p", "smooth"),
span = .5,
layout = c(4, 3))##################################
# Formulating the box plot
##################################
featurePlot(x = PME.Numeric,
y = PME$GENDER,
plot = "box",
scales = list(x = list(relation="free", rot = 90),
y = list(relation="free")),
adjust = 1.5,
layout = c(4, 3))##################################
# Formulating the box plot
##################################
featurePlot(x = PME.Numeric,
y = PME$CONTIN,
plot = "box",
scales = list(x = list(relation="free", rot = 90),
y = list(relation="free")),
adjust = 1.5,
layout = c(4, 3))##################################
# Evaluating model-independent
# feature importance metrics
##################################
##################################
# Obtaining the LOWESSPR pseudo-R-Squared
##################################
FE_LOWESSPR <- filterVarImp(x = PME.Numeric[,!names(PME.Numeric) %in% c("LIFEXP")],
y = PME$LIFEXP,
nonpara = TRUE)
##################################
# Formulating the summary table
##################################
FE_LOWESSPR_Summary <- FE_LOWESSPR
FE_LOWESSPR_Summary$Predictor <- rownames(FE_LOWESSPR)
names(FE_LOWESSPR_Summary)[1] <- "LOWESSPR"
FE_LOWESSPR_Summary$Metric <- rep("LOWESSPR",nrow(FE_LOWESSPR))
FE_LOWESSPR_Summary## LOWESSPR Predictor Metric
## UNEMPR 0.03707208 UNEMPR LOWESSPR
## INFMOR 0.82546121 INFMOR LOWESSPR
## GDP 0.25427692 GDP LOWESSPR
## CLTECH 0.58186548 CLTECH LOWESSPR
## PERCAP 0.62170474 PERCAP LOWESSPR
## RTIMOR 0.51428354 RTIMOR LOWESSPR
## HEPIMM 0.18618812 HEPIMM LOWESSPR
## HOSBED 0.34642501 HOSBED LOWESSPR
## RURPOP 0.35023499 RURPOP LOWESSPR
## NCOMOR 0.59655877 NCOMOR LOWESSPR
##################################
# Exploring predictor performance
# using LOWESS
##################################
dotplot(Predictor ~ LOWESSPR | Metric,
FE_LOWESSPR_Summary,
origin = 0,
type = c("p", "h"),
pch = 16,
cex = 2,
alpha = 0.45,
prepanel = function(x, y) {
list(ylim = levels(reorder(y, x)))
},
panel = function(x, y, ...) {
panel.dotplot(x, reorder(y, x), ...)
})##################################
# Obtaining the Pearson correlation coefficient
##################################
(FE_PCC <- abs(cor(PME.Numeric, method="pearson")[-10,10]))## UNEMPR INFMOR GDP CLTECH PERCAP RTIMOR HEPIMM
## 0.04661692 0.63238609 0.48959856 0.50254656 0.59491536 0.35048089 0.19826820
## HOSBED RURPOP LIFEXP
## 0.24448432 0.53019287 0.73325984
##################################
# Formulating the summary table
##################################
FE_PCC_Summary <- data.frame(Predictor = names(PME.Numeric)[1:(ncol(PME.Numeric)-1)],
PCC = FE_PCC,
Metric = rep("PCC", length(FE_PCC)))
FE_PCC_Summary## Predictor PCC Metric
## UNEMPR UNEMPR 0.04661692 PCC
## INFMOR INFMOR 0.63238609 PCC
## GDP GDP 0.48959856 PCC
## CLTECH CLTECH 0.50254656 PCC
## PERCAP PERCAP 0.59491536 PCC
## RTIMOR RTIMOR 0.35048089 PCC
## HEPIMM HEPIMM 0.19826820 PCC
## HOSBED HOSBED 0.24448432 PCC
## RURPOP RURPOP 0.53019287 PCC
## LIFEXP NCOMOR 0.73325984 PCC
##################################
# Exploring predictor performance
# using PCC
##################################
dotplot(Predictor ~ PCC | Metric,
FE_PCC_Summary,
origin = 0,
type = c("p", "h"),
pch = 16,
cex = 2,
alpha = 0.45,
prepanel = function(x, y) {
list(ylim = levels(reorder(y, x)))
},
panel = function(x, y, ...) {
panel.dotplot(x, reorder(y, x), ...)
})##################################
# Obtaining the Spearman's rank correlation coefficient
##################################
(FE_SRCC <- abs(cor(PME.Numeric, method="spearman")[-10,10]))## UNEMPR INFMOR GDP CLTECH PERCAP RTIMOR HEPIMM
## 0.04597079 0.63560349 0.47632718 0.56524903 0.60047700 0.40980009 0.18007328
## HOSBED RURPOP LIFEXP
## 0.26303483 0.54071785 0.78912805
##################################
# Formulating the summary table
##################################
FE_SRCC_Summary <- data.frame(Predictor = names(PME.Numeric)[1:(ncol(PME.Numeric)-1)],
SRCC = FE_SRCC,
Metric = rep("SRCC", length(FE_SRCC)))
FE_SRCC_Summary## Predictor SRCC Metric
## UNEMPR UNEMPR 0.04597079 SRCC
## INFMOR INFMOR 0.63560349 SRCC
## GDP GDP 0.47632718 SRCC
## CLTECH CLTECH 0.56524903 SRCC
## PERCAP PERCAP 0.60047700 SRCC
## RTIMOR RTIMOR 0.40980009 SRCC
## HEPIMM HEPIMM 0.18007328 SRCC
## HOSBED HOSBED 0.26303483 SRCC
## RURPOP RURPOP 0.54071785 SRCC
## LIFEXP NCOMOR 0.78912805 SRCC
##################################
# Exploring predictor performance
# using SRCC
##################################
dotplot(Predictor ~ SRCC | Metric,
FE_SRCC_Summary,
origin = 0,
type = c("p", "h"),
pch = 16,
cex = 2,
alpha = 0.45,
prepanel = function(x, y) {
list(ylim = levels(reorder(y, x)))
},
panel = function(x, y, ...) {
panel.dotplot(x, reorder(y, x), ...)
})##################################
# Obtaining the maximal information coefficient
##################################
FE_MIC <- mine(x = PME.Numeric[,!names(PME.Numeric) %in% c("LIFEXP")],
y = PME$LIFEXP)$MIC
##################################
# Formulating the summary table
##################################
FE_MIC_Summary <- data.frame(Predictor = names(PME.Numeric)[1:(ncol(PME.Numeric)-1)],
MIC = FE_MIC[,1],
Metric = rep("MIC", length(FE_MIC)))
FE_MIC_Summary## Predictor MIC Metric
## 1 UNEMPR 0.1911950 MIC
## 2 INFMOR 0.7083938 MIC
## 3 GDP 0.3234249 MIC
## 4 CLTECH 0.5099735 MIC
## 5 PERCAP 0.5502257 MIC
## 6 RTIMOR 0.4771528 MIC
## 7 HEPIMM 0.2652364 MIC
## 8 HOSBED 0.3711493 MIC
## 9 RURPOP 0.4097827 MIC
## 10 NCOMOR 0.6438989 MIC
##################################
# Exploring predictor performance
# using MIC
##################################
dotplot(Predictor ~ MIC | Metric,
FE_MIC_Summary,
origin = 0,
type = c("p", "h"),
pch = 16,
cex = 2,
alpha = 0.45,
prepanel = function(x, y) {
list(ylim = levels(reorder(y, x)))
},
panel = function(x, y, ...) {
panel.dotplot(x, reorder(y, x), ...)
})##################################
# Obtaining the relief values
##################################
FE_RV <- attrEval(LIFEXP ~ .,
data = PME.Numeric,
estimator = "RReliefFequalK")
##################################
# Formulating the summary table
##################################
FE_RV_Summary <- data.frame(Predictor = names(FE_RV),
RV = FE_RV,
Metric = rep("RV", length(FE_RV)))
FE_RV_Summary## Predictor RV Metric
## UNEMPR UNEMPR -0.01810789 RV
## INFMOR INFMOR 0.09365306 RV
## GDP GDP -0.09938056 RV
## CLTECH CLTECH 0.02105440 RV
## PERCAP PERCAP -0.04869230 RV
## RTIMOR RTIMOR 0.01046617 RV
## HEPIMM HEPIMM -0.03530648 RV
## HOSBED HOSBED -0.06597469 RV
## RURPOP RURPOP -0.10732377 RV
## NCOMOR NCOMOR 0.29910591 RV
##################################
# Exploring predictor performance
##################################
dotplot(Predictor ~ RV | Metric,
FE_RV_Summary,
origin = 0,
type = c("p", "h"),
pch = 16,
cex = 2,
alpha = 0.45,
prepanel = function(x, y) {
list(ylim = levels(reorder(y, x)))
},
panel = function(x, y, ...) {
panel.dotplot(x, reorder(y, x), ...)
})##################################
# Preparing the dataset for
# model development and test
##################################
set.seed(12345678)
trainIndex <- createDataPartition(PME$LIFEXP,
p = 0.8,
list = FALSE,
times = 1)
##################################
# Formulating the model development data
##################################
MD <- PME[ trainIndex,]
##################################
# Formulating the model test data
##################################
MT <- PME[-trainIndex,]
##################################
# Preparing the dataset for
# model development
##################################
MD <- MD[,c("GENDER","CONTIN","INFMOR","PERCAP","CLTECH","NCOMOR","LIFEXP")]
MD.Model.Predictors <- MD[,c("GENDER","CONTIN","INFMOR","PERCAP","CLTECH","NCOMOR")]
##################################
# Preparing the dataset for
# model test
##################################
MT <- MT[,c("GENDER","CONTIN","INFMOR","PERCAP","CLTECH","NCOMOR","LIFEXP")]
MT.Model.Predictors <- MT[,c("GENDER","CONTIN","INFMOR","PERCAP","CLTECH","NCOMOR")]
##################################
# Creating consistent fold assignments
# for the 10-Fold Cross Validation process
##################################
set.seed(12345678)
KFold_Indices <- createFolds(MD$LIFEXP,
k = 10,
returnTrain=TRUE)
KFold_Control <- trainControl(method="cv",
index=KFold_Indices)
##################################
# Defining the model hyperparameter values
# for the GBM model
##################################
GBM_Grid = expand.grid(n.trees = c(100, 200, 300),
interaction.depth = c(1, 3, 5),
shrinkage = c(0.10,0.05,0.01),
n.minobsinnode = c(15,10,5))
##################################
# Running the GBM model
# by setting the caret method to 'gbm'
##################################
set.seed(12345678)
GBM_Tune <- train(x = MD.Model.Predictors,
y = MD$LIFEXP,
method = "gbm",
tuneGrid = GBM_Grid,
trControl = KFold_Control)## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.0192 nan 0.0100 0.7763
## 2 60.2652 nan 0.0100 0.7757
## 3 59.4737 nan 0.0100 0.7510
## 4 58.7221 nan 0.0100 0.7598
## 5 57.9938 nan 0.0100 0.7048
## 6 57.2211 nan 0.0100 0.7026
## 7 56.5150 nan 0.0100 0.6792
## 8 55.8126 nan 0.0100 0.7336
## 9 55.1307 nan 0.0100 0.6511
## 10 54.4439 nan 0.0100 0.6352
## 20 48.4888 nan 0.0100 0.4724
## 40 38.9299 nan 0.0100 0.4255
## 60 31.8093 nan 0.0100 0.2926
## 80 26.3714 nan 0.0100 0.2027
## 100 22.1975 nan 0.0100 0.1622
## 120 18.9764 nan 0.0100 0.1006
## 140 16.3808 nan 0.0100 0.0852
## 160 14.4027 nan 0.0100 0.0871
## 180 12.7414 nan 0.0100 0.0528
## 200 11.4125 nan 0.0100 0.0519
## 220 10.2835 nan 0.0100 0.0410
## 240 9.3387 nan 0.0100 0.0411
## 260 8.5430 nan 0.0100 0.0392
## 280 7.8728 nan 0.0100 0.0167
## 300 7.2959 nan 0.0100 0.0184
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.0182 nan 0.0100 0.8361
## 2 60.2779 nan 0.0100 0.8155
## 3 59.5017 nan 0.0100 0.7081
## 4 58.7437 nan 0.0100 0.6873
## 5 57.9931 nan 0.0100 0.7436
## 6 57.2779 nan 0.0100 0.6835
## 7 56.5913 nan 0.0100 0.7197
## 8 55.9075 nan 0.0100 0.6813
## 9 55.2119 nan 0.0100 0.6398
## 10 54.5418 nan 0.0100 0.6806
## 20 48.5210 nan 0.0100 0.5257
## 40 39.0515 nan 0.0100 0.3873
## 60 31.8911 nan 0.0100 0.2684
## 80 26.6252 nan 0.0100 0.2079
## 100 22.3697 nan 0.0100 0.1577
## 120 19.1586 nan 0.0100 0.1278
## 140 16.6712 nan 0.0100 0.0969
## 160 14.6273 nan 0.0100 0.0773
## 180 12.9450 nan 0.0100 0.0487
## 200 11.5525 nan 0.0100 0.0149
## 220 10.3859 nan 0.0100 0.0366
## 240 9.4442 nan 0.0100 0.0302
## 260 8.6273 nan 0.0100 0.0306
## 280 7.9543 nan 0.0100 0.0267
## 300 7.3607 nan 0.0100 0.0204
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.0556 nan 0.0100 0.7589
## 2 60.3011 nan 0.0100 0.7607
## 3 59.5410 nan 0.0100 0.7503
## 4 58.8573 nan 0.0100 0.6862
## 5 58.0926 nan 0.0100 0.7456
## 6 57.3219 nan 0.0100 0.6947
## 7 56.5769 nan 0.0100 0.7003
## 8 55.8379 nan 0.0100 0.6442
## 9 55.1650 nan 0.0100 0.6792
## 10 54.5170 nan 0.0100 0.6986
## 20 48.4557 nan 0.0100 0.5444
## 40 39.1432 nan 0.0100 0.3891
## 60 32.1819 nan 0.0100 0.2893
## 80 26.7656 nan 0.0100 0.2214
## 100 22.5205 nan 0.0100 0.1801
## 120 19.2565 nan 0.0100 0.1308
## 140 16.6481 nan 0.0100 0.1039
## 160 14.5722 nan 0.0100 0.0879
## 180 12.9101 nan 0.0100 0.0668
## 200 11.4789 nan 0.0100 0.0583
## 220 10.3831 nan 0.0100 0.0379
## 240 9.4585 nan 0.0100 0.0361
## 260 8.6801 nan 0.0100 0.0311
## 280 8.0069 nan 0.0100 0.0218
## 300 7.4189 nan 0.0100 0.0258
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.7811 nan 0.0100 0.9879
## 2 59.7756 nan 0.0100 1.0245
## 3 58.7235 nan 0.0100 0.9914
## 4 57.7344 nan 0.0100 0.9273
## 5 56.8135 nan 0.0100 0.9757
## 6 55.8527 nan 0.0100 0.8346
## 7 54.9932 nan 0.0100 0.8278
## 8 54.0432 nan 0.0100 0.9012
## 9 53.1906 nan 0.0100 0.8415
## 10 52.3483 nan 0.0100 0.8122
## 20 44.4018 nan 0.0100 0.7897
## 40 32.5229 nan 0.0100 0.4547
## 60 24.4382 nan 0.0100 0.2989
## 80 18.6018 nan 0.0100 0.2411
## 100 14.5876 nan 0.0100 0.1549
## 120 11.6671 nan 0.0100 0.1217
## 140 9.5683 nan 0.0100 0.0590
## 160 7.9564 nan 0.0100 0.0660
## 180 6.7628 nan 0.0100 0.0471
## 200 5.8981 nan 0.0100 0.0302
## 220 5.2091 nan 0.0100 0.0163
## 240 4.7261 nan 0.0100 0.0181
## 260 4.3201 nan 0.0100 0.0122
## 280 4.0142 nan 0.0100 0.0040
## 300 3.7828 nan 0.0100 0.0046
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.7363 nan 0.0100 0.9393
## 2 59.7677 nan 0.0100 0.9532
## 3 58.8018 nan 0.0100 0.9275
## 4 57.8559 nan 0.0100 0.9172
## 5 56.8602 nan 0.0100 1.0358
## 6 55.8760 nan 0.0100 0.9584
## 7 54.9599 nan 0.0100 0.9441
## 8 54.0550 nan 0.0100 0.8263
## 9 53.1614 nan 0.0100 0.8152
## 10 52.3250 nan 0.0100 0.9347
## 20 44.3860 nan 0.0100 0.7844
## 40 32.2747 nan 0.0100 0.4327
## 60 24.1659 nan 0.0100 0.3232
## 80 18.3571 nan 0.0100 0.2159
## 100 14.3483 nan 0.0100 0.1701
## 120 11.5081 nan 0.0100 0.1229
## 140 9.4528 nan 0.0100 0.0710
## 160 7.9033 nan 0.0100 0.0642
## 180 6.7763 nan 0.0100 0.0325
## 200 5.9195 nan 0.0100 0.0272
## 220 5.2733 nan 0.0100 0.0241
## 240 4.7617 nan 0.0100 0.0130
## 260 4.4171 nan 0.0100 0.0155
## 280 4.1279 nan 0.0100 0.0100
## 300 3.8959 nan 0.0100 0.0051
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.7823 nan 0.0100 0.9654
## 2 59.8273 nan 0.0100 0.9559
## 3 58.8419 nan 0.0100 1.0246
## 4 57.8789 nan 0.0100 0.8718
## 5 56.9219 nan 0.0100 0.8905
## 6 55.9560 nan 0.0100 0.9847
## 7 55.0535 nan 0.0100 0.8720
## 8 54.1138 nan 0.0100 0.8692
## 9 53.2551 nan 0.0100 0.9301
## 10 52.4563 nan 0.0100 0.8490
## 20 44.6100 nan 0.0100 0.7685
## 40 32.8376 nan 0.0100 0.4628
## 60 24.6223 nan 0.0100 0.3640
## 80 18.8576 nan 0.0100 0.2061
## 100 14.7512 nan 0.0100 0.1434
## 120 11.8363 nan 0.0100 0.0932
## 140 9.6990 nan 0.0100 0.1010
## 160 8.1543 nan 0.0100 0.0668
## 180 6.9782 nan 0.0100 0.0413
## 200 6.0937 nan 0.0100 0.0303
## 220 5.4293 nan 0.0100 0.0227
## 240 4.9297 nan 0.0100 0.0183
## 260 4.5575 nan 0.0100 0.0107
## 280 4.2786 nan 0.0100 0.0083
## 300 4.0603 nan 0.0100 0.0037
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.7301 nan 0.0100 1.0291
## 2 59.6424 nan 0.0100 1.0195
## 3 58.5884 nan 0.0100 0.8919
## 4 57.5610 nan 0.0100 0.8836
## 5 56.5464 nan 0.0100 1.0641
## 6 55.5833 nan 0.0100 0.8985
## 7 54.6208 nan 0.0100 0.8715
## 8 53.6729 nan 0.0100 0.8315
## 9 52.7461 nan 0.0100 0.9069
## 10 51.8164 nan 0.0100 0.8645
## 20 43.5196 nan 0.0100 0.7079
## 40 31.1126 nan 0.0100 0.5218
## 60 22.6816 nan 0.0100 0.3357
## 80 16.9224 nan 0.0100 0.2170
## 100 12.8284 nan 0.0100 0.1488
## 120 10.0438 nan 0.0100 0.1165
## 140 7.9798 nan 0.0100 0.0732
## 160 6.5047 nan 0.0100 0.0622
## 180 5.4528 nan 0.0100 0.0331
## 200 4.7095 nan 0.0100 0.0304
## 220 4.1500 nan 0.0100 0.0162
## 240 3.7521 nan 0.0100 0.0085
## 260 3.4586 nan 0.0100 0.0048
## 280 3.2304 nan 0.0100 0.0034
## 300 3.0364 nan 0.0100 0.0014
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.7447 nan 0.0100 1.0219
## 2 59.7039 nan 0.0100 1.0660
## 3 58.6502 nan 0.0100 1.0398
## 4 57.6297 nan 0.0100 1.0546
## 5 56.6425 nan 0.0100 1.0440
## 6 55.6402 nan 0.0100 1.0016
## 7 54.6664 nan 0.0100 0.9037
## 8 53.7131 nan 0.0100 0.9369
## 9 52.7940 nan 0.0100 0.9479
## 10 51.8332 nan 0.0100 0.9079
## 20 43.5255 nan 0.0100 0.7096
## 40 31.1589 nan 0.0100 0.4859
## 60 22.7958 nan 0.0100 0.3179
## 80 16.9616 nan 0.0100 0.2104
## 100 12.9139 nan 0.0100 0.1560
## 120 10.0784 nan 0.0100 0.1178
## 140 8.0651 nan 0.0100 0.0728
## 160 6.6455 nan 0.0100 0.0470
## 180 5.6044 nan 0.0100 0.0368
## 200 4.8571 nan 0.0100 0.0313
## 220 4.3103 nan 0.0100 0.0125
## 240 3.9046 nan 0.0100 0.0112
## 260 3.5932 nan 0.0100 0.0096
## 280 3.3656 nan 0.0100 0.0025
## 300 3.1936 nan 0.0100 0.0013
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.7644 nan 0.0100 1.0981
## 2 59.6868 nan 0.0100 1.0994
## 3 58.6746 nan 0.0100 1.0847
## 4 57.6006 nan 0.0100 1.1392
## 5 56.6122 nan 0.0100 0.9589
## 6 55.6823 nan 0.0100 1.0254
## 7 54.6742 nan 0.0100 1.0423
## 8 53.7607 nan 0.0100 0.9721
## 9 52.8951 nan 0.0100 0.9395
## 10 52.0213 nan 0.0100 0.9194
## 20 43.8354 nan 0.0100 0.6542
## 40 31.4738 nan 0.0100 0.5421
## 60 23.1368 nan 0.0100 0.3651
## 80 17.2716 nan 0.0100 0.2035
## 100 13.1896 nan 0.0100 0.1619
## 120 10.3864 nan 0.0100 0.1076
## 140 8.4033 nan 0.0100 0.0712
## 160 6.9817 nan 0.0100 0.0588
## 180 6.0054 nan 0.0100 0.0388
## 200 5.2517 nan 0.0100 0.0227
## 220 4.6931 nan 0.0100 0.0165
## 240 4.3071 nan 0.0100 0.0064
## 260 4.0008 nan 0.0100 0.0025
## 280 3.7852 nan 0.0100 0.0075
## 300 3.6251 nan 0.0100 0.0016
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.0344 nan 0.0500 3.8415
## 2 54.5365 nan 0.0500 3.4273
## 3 51.7720 nan 0.0500 3.1884
## 4 49.0894 nan 0.0500 2.7612
## 5 46.2533 nan 0.0500 2.6837
## 6 43.7354 nan 0.0500 2.4708
## 7 41.2809 nan 0.0500 2.1635
## 8 39.2262 nan 0.0500 2.1419
## 9 37.0853 nan 0.0500 2.2079
## 10 35.1301 nan 0.0500 1.9341
## 20 22.0217 nan 0.0500 0.6677
## 40 11.3559 nan 0.0500 0.2919
## 60 7.2935 nan 0.0500 0.1375
## 80 5.4364 nan 0.0500 0.0672
## 100 4.5723 nan 0.0500 -0.0144
## 120 4.1502 nan 0.0500 0.0075
## 140 3.9273 nan 0.0500 -0.0055
## 160 3.7719 nan 0.0500 -0.0000
## 180 3.6672 nan 0.0500 -0.0064
## 200 3.5875 nan 0.0500 -0.0060
## 220 3.5093 nan 0.0500 -0.0159
## 240 3.4362 nan 0.0500 -0.0065
## 260 3.3752 nan 0.0500 -0.0091
## 280 3.3165 nan 0.0500 -0.0038
## 300 3.2673 nan 0.0500 -0.0031
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.9628 nan 0.0500 3.7315
## 2 54.6727 nan 0.0500 3.4527
## 3 51.5215 nan 0.0500 3.1737
## 4 48.4679 nan 0.0500 2.9660
## 5 45.6125 nan 0.0500 2.6173
## 6 43.1555 nan 0.0500 2.3758
## 7 40.6447 nan 0.0500 2.3076
## 8 38.6491 nan 0.0500 1.7343
## 9 36.8462 nan 0.0500 1.6289
## 10 35.3466 nan 0.0500 1.4689
## 20 22.3990 nan 0.0500 0.4800
## 40 11.4911 nan 0.0500 0.2501
## 60 7.4326 nan 0.0500 0.1619
## 80 5.5370 nan 0.0500 0.0593
## 100 4.6345 nan 0.0500 0.0070
## 120 4.1969 nan 0.0500 0.0073
## 140 3.9968 nan 0.0500 0.0072
## 160 3.8581 nan 0.0500 -0.0145
## 180 3.7538 nan 0.0500 -0.0117
## 200 3.6598 nan 0.0500 -0.0090
## 220 3.5888 nan 0.0500 -0.0061
## 240 3.5186 nan 0.0500 -0.0077
## 260 3.4551 nan 0.0500 -0.0162
## 280 3.4013 nan 0.0500 -0.0084
## 300 3.3618 nan 0.0500 -0.0169
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.6228 nan 0.0500 3.9987
## 2 54.1330 nan 0.0500 3.3888
## 3 51.0764 nan 0.0500 2.9593
## 4 48.2039 nan 0.0500 2.5425
## 5 45.4965 nan 0.0500 2.7583
## 6 42.7987 nan 0.0500 2.4621
## 7 40.5409 nan 0.0500 2.2391
## 8 38.5091 nan 0.0500 2.0108
## 9 36.5919 nan 0.0500 2.1784
## 10 34.6881 nan 0.0500 1.8360
## 20 21.8050 nan 0.0500 0.8761
## 40 11.3694 nan 0.0500 0.2106
## 60 7.4091 nan 0.0500 0.0972
## 80 5.6757 nan 0.0500 0.0369
## 100 4.7825 nan 0.0500 0.0123
## 120 4.3704 nan 0.0500 -0.0012
## 140 4.1583 nan 0.0500 -0.0018
## 160 4.0575 nan 0.0500 -0.0117
## 180 3.9493 nan 0.0500 -0.0036
## 200 3.8721 nan 0.0500 -0.0035
## 220 3.7928 nan 0.0500 -0.0082
## 240 3.7154 nan 0.0500 -0.0074
## 260 3.6505 nan 0.0500 -0.0039
## 280 3.5909 nan 0.0500 -0.0066
## 300 3.5425 nan 0.0500 -0.0068
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.5954 nan 0.0500 5.0099
## 2 51.9922 nan 0.0500 4.5935
## 3 47.6779 nan 0.0500 3.7401
## 4 43.9259 nan 0.0500 4.2016
## 5 40.4580 nan 0.0500 3.1794
## 6 37.3158 nan 0.0500 2.5898
## 7 34.4163 nan 0.0500 2.8136
## 8 31.9373 nan 0.0500 2.3894
## 9 29.6482 nan 0.0500 2.1312
## 10 27.6422 nan 0.0500 2.1355
## 20 14.0346 nan 0.0500 0.6351
## 40 5.8839 nan 0.0500 0.1299
## 60 3.7923 nan 0.0500 0.0253
## 80 3.1408 nan 0.0500 -0.0048
## 100 2.8130 nan 0.0500 -0.0304
## 120 2.5901 nan 0.0500 -0.0085
## 140 2.3987 nan 0.0500 -0.0198
## 160 2.2314 nan 0.0500 -0.0156
## 180 2.0850 nan 0.0500 -0.0075
## 200 1.9742 nan 0.0500 -0.0069
## 220 1.8957 nan 0.0500 -0.0090
## 240 1.8090 nan 0.0500 -0.0129
## 260 1.7282 nan 0.0500 -0.0128
## 280 1.6537 nan 0.0500 -0.0055
## 300 1.5882 nan 0.0500 -0.0053
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.7704 nan 0.0500 5.1293
## 2 52.0636 nan 0.0500 4.1257
## 3 47.9781 nan 0.0500 3.9736
## 4 44.2478 nan 0.0500 3.7691
## 5 41.0279 nan 0.0500 3.4203
## 6 37.9647 nan 0.0500 2.9650
## 7 35.0765 nan 0.0500 2.9375
## 8 32.3973 nan 0.0500 2.5871
## 9 30.1281 nan 0.0500 2.3746
## 10 28.0278 nan 0.0500 2.2176
## 20 14.2885 nan 0.0500 0.8064
## 40 5.8812 nan 0.0500 0.1339
## 60 3.9463 nan 0.0500 0.0309
## 80 3.3117 nan 0.0500 -0.0012
## 100 3.0017 nan 0.0500 0.0001
## 120 2.8043 nan 0.0500 -0.0145
## 140 2.6628 nan 0.0500 -0.0136
## 160 2.5457 nan 0.0500 -0.0163
## 180 2.4306 nan 0.0500 -0.0114
## 200 2.3195 nan 0.0500 -0.0038
## 220 2.2270 nan 0.0500 -0.0206
## 240 2.1431 nan 0.0500 -0.0173
## 260 2.0566 nan 0.0500 -0.0136
## 280 1.9931 nan 0.0500 -0.0136
## 300 1.9436 nan 0.0500 -0.0095
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.5645 nan 0.0500 5.1224
## 2 51.9284 nan 0.0500 3.8676
## 3 48.0714 nan 0.0500 3.8581
## 4 44.3610 nan 0.0500 3.8282
## 5 40.8656 nan 0.0500 3.4710
## 6 37.6051 nan 0.0500 3.2033
## 7 34.8928 nan 0.0500 2.7928
## 8 32.3792 nan 0.0500 2.3185
## 9 30.1353 nan 0.0500 2.5385
## 10 28.0992 nan 0.0500 1.9273
## 20 14.5747 nan 0.0500 0.7630
## 40 5.9520 nan 0.0500 0.1248
## 60 4.1205 nan 0.0500 0.0301
## 80 3.5689 nan 0.0500 -0.0043
## 100 3.2480 nan 0.0500 -0.0127
## 120 3.0308 nan 0.0500 -0.0122
## 140 2.8719 nan 0.0500 -0.0176
## 160 2.7390 nan 0.0500 -0.0111
## 180 2.6283 nan 0.0500 -0.0092
## 200 2.5264 nan 0.0500 -0.0211
## 220 2.4331 nan 0.0500 -0.0157
## 240 2.3688 nan 0.0500 -0.0182
## 260 2.2677 nan 0.0500 -0.0081
## 280 2.2001 nan 0.0500 -0.0168
## 300 2.1255 nan 0.0500 -0.0103
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.5588 nan 0.0500 4.7218
## 2 51.5893 nan 0.0500 4.9800
## 3 47.1904 nan 0.0500 4.5704
## 4 43.1887 nan 0.0500 3.4471
## 5 39.5545 nan 0.0500 3.8226
## 6 36.4772 nan 0.0500 3.1387
## 7 33.6011 nan 0.0500 2.6734
## 8 30.9170 nan 0.0500 2.4560
## 9 28.6029 nan 0.0500 2.2412
## 10 26.5061 nan 0.0500 2.0475
## 20 12.6805 nan 0.0500 0.8432
## 40 4.6380 nan 0.0500 0.0789
## 60 3.0019 nan 0.0500 0.0182
## 80 2.4495 nan 0.0500 -0.0166
## 100 2.1448 nan 0.0500 -0.0199
## 120 1.9072 nan 0.0500 -0.0127
## 140 1.7133 nan 0.0500 -0.0077
## 160 1.5620 nan 0.0500 -0.0124
## 180 1.4178 nan 0.0500 -0.0161
## 200 1.3135 nan 0.0500 -0.0071
## 220 1.2133 nan 0.0500 -0.0081
## 240 1.1287 nan 0.0500 -0.0124
## 260 1.0543 nan 0.0500 -0.0106
## 280 0.9757 nan 0.0500 -0.0066
## 300 0.9178 nan 0.0500 -0.0085
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.5683 nan 0.0500 4.9344
## 2 51.6944 nan 0.0500 4.8168
## 3 47.2959 nan 0.0500 4.3846
## 4 43.5104 nan 0.0500 3.7302
## 5 39.7695 nan 0.0500 3.7444
## 6 36.6010 nan 0.0500 3.1937
## 7 33.6638 nan 0.0500 2.6948
## 8 30.9358 nan 0.0500 2.6595
## 9 28.4562 nan 0.0500 2.1274
## 10 26.2043 nan 0.0500 2.0773
## 20 12.7507 nan 0.0500 0.6586
## 40 4.8954 nan 0.0500 0.1259
## 60 3.2180 nan 0.0500 -0.0104
## 80 2.7147 nan 0.0500 -0.0113
## 100 2.4543 nan 0.0500 -0.0134
## 120 2.2894 nan 0.0500 -0.0185
## 140 2.1054 nan 0.0500 -0.0231
## 160 1.9550 nan 0.0500 -0.0053
## 180 1.8195 nan 0.0500 -0.0263
## 200 1.6884 nan 0.0500 -0.0134
## 220 1.5804 nan 0.0500 -0.0132
## 240 1.4846 nan 0.0500 -0.0204
## 260 1.4026 nan 0.0500 -0.0103
## 280 1.3298 nan 0.0500 -0.0055
## 300 1.2638 nan 0.0500 -0.0054
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.2670 nan 0.0500 4.9296
## 2 51.4201 nan 0.0500 4.4277
## 3 47.1252 nan 0.0500 4.1616
## 4 43.0992 nan 0.0500 4.4701
## 5 39.5475 nan 0.0500 3.2675
## 6 36.3316 nan 0.0500 2.9151
## 7 33.4274 nan 0.0500 2.7498
## 8 30.8849 nan 0.0500 2.4451
## 9 28.5181 nan 0.0500 2.3685
## 10 26.2967 nan 0.0500 2.2567
## 20 12.7991 nan 0.0500 0.7365
## 40 5.1023 nan 0.0500 0.1075
## 60 3.5319 nan 0.0500 0.0196
## 80 3.0871 nan 0.0500 -0.0160
## 100 2.8119 nan 0.0500 -0.0116
## 120 2.6216 nan 0.0500 -0.0075
## 140 2.4403 nan 0.0500 -0.0120
## 160 2.3001 nan 0.0500 -0.0060
## 180 2.1775 nan 0.0500 -0.0142
## 200 2.0699 nan 0.0500 -0.0169
## 220 1.9702 nan 0.0500 -0.0133
## 240 1.8870 nan 0.0500 -0.0101
## 260 1.7939 nan 0.0500 -0.0079
## 280 1.7193 nan 0.0500 -0.0083
## 300 1.6625 nan 0.0500 -0.0082
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.3161 nan 0.1000 7.5967
## 2 48.5321 nan 0.1000 5.9673
## 3 43.3385 nan 0.1000 4.9340
## 4 38.8289 nan 0.1000 4.1032
## 5 34.7630 nan 0.1000 3.8697
## 6 31.3677 nan 0.1000 3.5022
## 7 28.3541 nan 0.1000 2.5892
## 8 25.8490 nan 0.1000 2.3528
## 9 23.3261 nan 0.1000 1.9713
## 10 21.4953 nan 0.1000 1.7198
## 20 11.0330 nan 0.1000 0.5097
## 40 5.4589 nan 0.1000 0.1066
## 60 4.1032 nan 0.1000 0.0118
## 80 3.8045 nan 0.1000 0.0068
## 100 3.6322 nan 0.1000 -0.0227
## 120 3.4930 nan 0.1000 -0.0226
## 140 3.3736 nan 0.1000 -0.0234
## 160 3.3006 nan 0.1000 -0.0176
## 180 3.2299 nan 0.1000 -0.0281
## 200 3.1753 nan 0.1000 -0.0066
## 220 3.1126 nan 0.1000 -0.0165
## 240 3.0482 nan 0.1000 -0.0135
## 260 2.9993 nan 0.1000 -0.0278
## 280 2.9428 nan 0.1000 -0.0157
## 300 2.8895 nan 0.1000 -0.0200
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.8221 nan 0.1000 7.5962
## 2 48.1017 nan 0.1000 6.2012
## 3 42.4392 nan 0.1000 5.1381
## 4 38.6368 nan 0.1000 3.6170
## 5 34.6672 nan 0.1000 3.9919
## 6 31.3575 nan 0.1000 2.6233
## 7 28.5477 nan 0.1000 2.7536
## 8 26.2321 nan 0.1000 2.1725
## 9 23.9798 nan 0.1000 2.2318
## 10 22.0617 nan 0.1000 1.8792
## 20 11.0964 nan 0.1000 0.5872
## 40 5.3627 nan 0.1000 0.0595
## 60 4.1855 nan 0.1000 0.0325
## 80 3.8638 nan 0.1000 -0.0056
## 100 3.6775 nan 0.1000 -0.0361
## 120 3.5706 nan 0.1000 -0.0058
## 140 3.4825 nan 0.1000 -0.0022
## 160 3.4043 nan 0.1000 -0.0162
## 180 3.3384 nan 0.1000 -0.0236
## 200 3.2481 nan 0.1000 -0.0083
## 220 3.1899 nan 0.1000 -0.0293
## 240 3.1343 nan 0.1000 -0.0229
## 260 3.0677 nan 0.1000 -0.0037
## 280 3.0078 nan 0.1000 -0.0093
## 300 2.9503 nan 0.1000 -0.0101
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.6917 nan 0.1000 7.4753
## 2 47.9111 nan 0.1000 6.1290
## 3 42.9440 nan 0.1000 4.9682
## 4 38.6625 nan 0.1000 4.4414
## 5 34.9776 nan 0.1000 3.7037
## 6 31.3760 nan 0.1000 3.4863
## 7 28.5369 nan 0.1000 3.1025
## 8 26.3977 nan 0.1000 1.7627
## 9 23.8295 nan 0.1000 2.4272
## 10 21.8973 nan 0.1000 1.9409
## 20 10.9792 nan 0.1000 0.4816
## 40 5.5861 nan 0.1000 0.0929
## 60 4.4016 nan 0.1000 0.0188
## 80 4.0705 nan 0.1000 -0.0102
## 100 3.9154 nan 0.1000 -0.0088
## 120 3.7406 nan 0.1000 -0.0130
## 140 3.6276 nan 0.1000 -0.0231
## 160 3.5139 nan 0.1000 -0.0102
## 180 3.4294 nan 0.1000 -0.0058
## 200 3.3450 nan 0.1000 -0.0184
## 220 3.2711 nan 0.1000 -0.0101
## 240 3.2038 nan 0.1000 -0.0085
## 260 3.1537 nan 0.1000 -0.0276
## 280 3.0923 nan 0.1000 -0.0144
## 300 3.0416 nan 0.1000 -0.0053
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.4726 nan 0.1000 8.9648
## 2 43.8795 nan 0.1000 7.7489
## 3 37.5948 nan 0.1000 6.4576
## 4 32.3294 nan 0.1000 4.7621
## 5 27.5584 nan 0.1000 4.6246
## 6 23.9344 nan 0.1000 3.6162
## 7 20.8036 nan 0.1000 2.9801
## 8 18.0210 nan 0.1000 2.4732
## 9 15.6881 nan 0.1000 2.0917
## 10 14.1115 nan 0.1000 1.4837
## 20 5.7824 nan 0.1000 0.2826
## 40 3.1788 nan 0.1000 -0.0073
## 60 2.6056 nan 0.1000 0.0038
## 80 2.2704 nan 0.1000 -0.0222
## 100 2.0300 nan 0.1000 -0.0106
## 120 1.7811 nan 0.1000 -0.0285
## 140 1.6086 nan 0.1000 -0.0210
## 160 1.4618 nan 0.1000 -0.0226
## 180 1.3599 nan 0.1000 -0.0113
## 200 1.2608 nan 0.1000 -0.0064
## 220 1.1921 nan 0.1000 -0.0145
## 240 1.1012 nan 0.1000 -0.0119
## 260 1.0217 nan 0.1000 -0.0081
## 280 0.9546 nan 0.1000 -0.0104
## 300 0.8919 nan 0.1000 -0.0093
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.2399 nan 0.1000 10.0455
## 2 43.9704 nan 0.1000 7.9486
## 3 38.2288 nan 0.1000 5.2999
## 4 32.3777 nan 0.1000 5.3854
## 5 27.5554 nan 0.1000 4.6569
## 6 23.9197 nan 0.1000 3.8108
## 7 20.9706 nan 0.1000 2.7298
## 8 18.4633 nan 0.1000 2.4062
## 9 15.9500 nan 0.1000 2.1532
## 10 14.1225 nan 0.1000 1.6851
## 20 5.7424 nan 0.1000 0.4124
## 40 3.2993 nan 0.1000 0.0136
## 60 2.7642 nan 0.1000 -0.0247
## 80 2.4472 nan 0.1000 -0.0236
## 100 2.2731 nan 0.1000 -0.0373
## 120 2.1058 nan 0.1000 -0.0137
## 140 1.9387 nan 0.1000 -0.0344
## 160 1.8227 nan 0.1000 -0.0310
## 180 1.6896 nan 0.1000 -0.0236
## 200 1.5886 nan 0.1000 -0.0240
## 220 1.5046 nan 0.1000 -0.0166
## 240 1.4342 nan 0.1000 -0.0292
## 260 1.3655 nan 0.1000 -0.0166
## 280 1.3094 nan 0.1000 -0.0106
## 300 1.2528 nan 0.1000 -0.0109
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.1985 nan 0.1000 8.9546
## 2 44.2912 nan 0.1000 7.8004
## 3 37.7013 nan 0.1000 6.5905
## 4 32.1139 nan 0.1000 4.7618
## 5 27.5704 nan 0.1000 3.8246
## 6 23.8799 nan 0.1000 3.6257
## 7 20.7950 nan 0.1000 3.0542
## 8 18.0271 nan 0.1000 2.5835
## 9 15.8764 nan 0.1000 1.9822
## 10 14.1450 nan 0.1000 1.6344
## 20 6.0919 nan 0.1000 0.2129
## 40 3.5881 nan 0.1000 -0.0376
## 60 3.1061 nan 0.1000 -0.0077
## 80 2.7711 nan 0.1000 -0.0325
## 100 2.5421 nan 0.1000 -0.0315
## 120 2.3426 nan 0.1000 -0.0529
## 140 2.1926 nan 0.1000 -0.0435
## 160 2.0785 nan 0.1000 -0.0148
## 180 1.9620 nan 0.1000 -0.0220
## 200 1.8454 nan 0.1000 -0.0152
## 220 1.7456 nan 0.1000 -0.0089
## 240 1.6746 nan 0.1000 -0.0196
## 260 1.6048 nan 0.1000 -0.0261
## 280 1.5287 nan 0.1000 -0.0253
## 300 1.4665 nan 0.1000 -0.0095
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.7145 nan 0.1000 10.3756
## 2 43.0919 nan 0.1000 8.2583
## 3 36.2916 nan 0.1000 6.1223
## 4 30.4362 nan 0.1000 5.4657
## 5 26.1032 nan 0.1000 5.0175
## 6 22.2192 nan 0.1000 4.0364
## 7 19.0044 nan 0.1000 2.7087
## 8 16.5323 nan 0.1000 2.2295
## 9 14.4749 nan 0.1000 1.7116
## 10 12.6749 nan 0.1000 1.8570
## 20 4.8160 nan 0.1000 0.2611
## 40 2.5902 nan 0.1000 -0.0003
## 60 2.0046 nan 0.1000 -0.0276
## 80 1.6307 nan 0.1000 -0.0076
## 100 1.4037 nan 0.1000 -0.0251
## 120 1.2264 nan 0.1000 -0.0189
## 140 1.0591 nan 0.1000 -0.0151
## 160 0.9383 nan 0.1000 -0.0214
## 180 0.8296 nan 0.1000 -0.0079
## 200 0.7226 nan 0.1000 -0.0169
## 220 0.6533 nan 0.1000 -0.0094
## 240 0.5826 nan 0.1000 -0.0124
## 260 0.5241 nan 0.1000 -0.0110
## 280 0.4698 nan 0.1000 -0.0047
## 300 0.4292 nan 0.1000 -0.0101
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.6871 nan 0.1000 9.4432
## 2 43.1760 nan 0.1000 8.5284
## 3 36.2803 nan 0.1000 7.2351
## 4 30.6930 nan 0.1000 5.7188
## 5 26.0779 nan 0.1000 4.5128
## 6 22.0238 nan 0.1000 3.9601
## 7 19.0725 nan 0.1000 2.9886
## 8 16.4103 nan 0.1000 2.5538
## 9 14.3840 nan 0.1000 1.8303
## 10 12.4843 nan 0.1000 1.7952
## 20 4.8169 nan 0.1000 0.2098
## 40 2.7760 nan 0.1000 0.0002
## 60 2.2630 nan 0.1000 -0.0166
## 80 1.9706 nan 0.1000 -0.0431
## 100 1.7534 nan 0.1000 -0.0199
## 120 1.5730 nan 0.1000 -0.0319
## 140 1.4006 nan 0.1000 -0.0157
## 160 1.2674 nan 0.1000 -0.0187
## 180 1.1430 nan 0.1000 -0.0192
## 200 1.0472 nan 0.1000 -0.0167
## 220 0.9740 nan 0.1000 -0.0158
## 240 0.8996 nan 0.1000 -0.0077
## 260 0.8109 nan 0.1000 -0.0153
## 280 0.7482 nan 0.1000 -0.0095
## 300 0.7035 nan 0.1000 -0.0123
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.5345 nan 0.1000 10.3054
## 2 43.0925 nan 0.1000 8.4375
## 3 36.0068 nan 0.1000 7.2820
## 4 30.4364 nan 0.1000 5.7147
## 5 25.4884 nan 0.1000 4.8591
## 6 21.7094 nan 0.1000 3.4260
## 7 18.6532 nan 0.1000 3.0285
## 8 16.0543 nan 0.1000 2.5504
## 9 14.1676 nan 0.1000 1.7930
## 10 12.3412 nan 0.1000 1.6605
## 20 5.0075 nan 0.1000 0.2380
## 40 3.1119 nan 0.1000 -0.0072
## 60 2.6684 nan 0.1000 -0.0126
## 80 2.4027 nan 0.1000 -0.0623
## 100 2.1318 nan 0.1000 -0.0220
## 120 1.9685 nan 0.1000 -0.0340
## 140 1.8308 nan 0.1000 -0.0214
## 160 1.6708 nan 0.1000 -0.0243
## 180 1.5475 nan 0.1000 -0.0271
## 200 1.4144 nan 0.1000 -0.0108
## 220 1.3058 nan 0.1000 -0.0215
## 240 1.2154 nan 0.1000 -0.0254
## 260 1.1360 nan 0.1000 -0.0090
## 280 1.0795 nan 0.1000 -0.0114
## 300 1.0089 nan 0.1000 -0.0138
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.2146 nan 0.0100 0.7571
## 2 60.4418 nan 0.0100 0.7667
## 3 59.6427 nan 0.0100 0.7732
## 4 58.8563 nan 0.0100 0.7111
## 5 58.1218 nan 0.0100 0.7164
## 6 57.3844 nan 0.0100 0.7175
## 7 56.6627 nan 0.0100 0.7603
## 8 55.9861 nan 0.0100 0.6582
## 9 55.3267 nan 0.0100 0.6970
## 10 54.6633 nan 0.0100 0.6140
## 20 48.7058 nan 0.0100 0.4878
## 40 39.0761 nan 0.0100 0.3942
## 60 31.8254 nan 0.0100 0.3262
## 80 26.2326 nan 0.0100 0.2038
## 100 22.0595 nan 0.0100 0.1801
## 120 18.8423 nan 0.0100 0.0819
## 140 16.3048 nan 0.0100 0.0959
## 160 14.1778 nan 0.0100 0.0775
## 180 12.5247 nan 0.0100 0.0552
## 200 11.1925 nan 0.0100 0.0533
## 220 10.0811 nan 0.0100 0.0429
## 240 9.1473 nan 0.0100 0.0298
## 260 8.3516 nan 0.0100 0.0244
## 280 7.6500 nan 0.0100 0.0266
## 300 7.0593 nan 0.0100 0.0240
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.2101 nan 0.0100 0.7058
## 2 60.4066 nan 0.0100 0.7338
## 3 59.6159 nan 0.0100 0.7538
## 4 58.9031 nan 0.0100 0.7667
## 5 58.2160 nan 0.0100 0.6699
## 6 57.4825 nan 0.0100 0.7249
## 7 56.7370 nan 0.0100 0.7177
## 8 56.0350 nan 0.0100 0.6768
## 9 55.3693 nan 0.0100 0.6461
## 10 54.7009 nan 0.0100 0.6582
## 20 48.5406 nan 0.0100 0.5734
## 40 39.1154 nan 0.0100 0.4282
## 60 31.9170 nan 0.0100 0.3021
## 80 26.4411 nan 0.0100 0.2322
## 100 22.1562 nan 0.0100 0.1720
## 120 18.9394 nan 0.0100 0.1307
## 140 16.2945 nan 0.0100 0.0892
## 160 14.2501 nan 0.0100 0.0950
## 180 12.6227 nan 0.0100 0.0704
## 200 11.2290 nan 0.0100 0.0646
## 220 10.1104 nan 0.0100 0.0350
## 240 9.1508 nan 0.0100 0.0344
## 260 8.3164 nan 0.0100 0.0368
## 280 7.6501 nan 0.0100 0.0299
## 300 7.0750 nan 0.0100 0.0142
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.2331 nan 0.0100 0.7881
## 2 60.4461 nan 0.0100 0.7510
## 3 59.6411 nan 0.0100 0.6633
## 4 58.8597 nan 0.0100 0.7500
## 5 58.1200 nan 0.0100 0.7374
## 6 57.4290 nan 0.0100 0.6743
## 7 56.7738 nan 0.0100 0.6543
## 8 56.1146 nan 0.0100 0.6179
## 9 55.4434 nan 0.0100 0.6502
## 10 54.7779 nan 0.0100 0.6477
## 20 48.7296 nan 0.0100 0.5429
## 40 39.1938 nan 0.0100 0.3492
## 60 31.7242 nan 0.0100 0.2906
## 80 26.2641 nan 0.0100 0.1913
## 100 22.0691 nan 0.0100 0.1638
## 120 18.7761 nan 0.0100 0.1298
## 140 16.2797 nan 0.0100 0.0982
## 160 14.2441 nan 0.0100 0.0761
## 180 12.5985 nan 0.0100 0.0769
## 200 11.2723 nan 0.0100 0.0487
## 220 10.1543 nan 0.0100 0.0372
## 240 9.1683 nan 0.0100 0.0372
## 260 8.3710 nan 0.0100 0.0340
## 280 7.7122 nan 0.0100 0.0182
## 300 7.1286 nan 0.0100 0.0171
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.9207 nan 0.0100 1.0617
## 2 59.9500 nan 0.0100 0.9774
## 3 58.9181 nan 0.0100 1.0423
## 4 57.9789 nan 0.0100 0.9182
## 5 57.0590 nan 0.0100 1.0044
## 6 56.1512 nan 0.0100 0.9246
## 7 55.1854 nan 0.0100 0.9386
## 8 54.2744 nan 0.0100 0.9058
## 9 53.4214 nan 0.0100 0.8462
## 10 52.5447 nan 0.0100 0.8344
## 20 44.5667 nan 0.0100 0.6811
## 40 32.5573 nan 0.0100 0.5254
## 60 24.2591 nan 0.0100 0.3446
## 80 18.4772 nan 0.0100 0.2190
## 100 14.3530 nan 0.0100 0.1786
## 120 11.3916 nan 0.0100 0.1027
## 140 9.3115 nan 0.0100 0.0759
## 160 7.7075 nan 0.0100 0.0542
## 180 6.5679 nan 0.0100 0.0291
## 200 5.6977 nan 0.0100 0.0229
## 220 5.0259 nan 0.0100 0.0228
## 240 4.5150 nan 0.0100 0.0219
## 260 4.1228 nan 0.0100 0.0128
## 280 3.8013 nan 0.0100 0.0099
## 300 3.5468 nan 0.0100 0.0063
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.9683 nan 0.0100 0.8833
## 2 59.9679 nan 0.0100 0.9525
## 3 58.9316 nan 0.0100 0.9552
## 4 57.9438 nan 0.0100 0.9942
## 5 56.9863 nan 0.0100 0.8982
## 6 56.0058 nan 0.0100 0.9342
## 7 55.1156 nan 0.0100 0.9748
## 8 54.2441 nan 0.0100 0.9168
## 9 53.3840 nan 0.0100 0.8944
## 10 52.5425 nan 0.0100 0.8226
## 20 44.7535 nan 0.0100 0.7515
## 40 32.6659 nan 0.0100 0.4574
## 60 24.3050 nan 0.0100 0.3278
## 80 18.5316 nan 0.0100 0.2395
## 100 14.5070 nan 0.0100 0.1561
## 120 11.5341 nan 0.0100 0.1093
## 140 9.4060 nan 0.0100 0.0801
## 160 7.8312 nan 0.0100 0.0603
## 180 6.6948 nan 0.0100 0.0406
## 200 5.8077 nan 0.0100 0.0251
## 220 5.1159 nan 0.0100 0.0230
## 240 4.5981 nan 0.0100 0.0181
## 260 4.2136 nan 0.0100 0.0110
## 280 3.9053 nan 0.0100 0.0112
## 300 3.6720 nan 0.0100 0.0048
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.0170 nan 0.0100 1.0234
## 2 59.9716 nan 0.0100 0.9814
## 3 58.9577 nan 0.0100 1.0491
## 4 57.9841 nan 0.0100 1.0418
## 5 57.0659 nan 0.0100 0.9546
## 6 56.0661 nan 0.0100 0.9480
## 7 55.0854 nan 0.0100 0.9326
## 8 54.1699 nan 0.0100 0.9218
## 9 53.2629 nan 0.0100 1.0079
## 10 52.3588 nan 0.0100 0.8554
## 20 44.5512 nan 0.0100 0.6441
## 40 32.5981 nan 0.0100 0.4712
## 60 24.3222 nan 0.0100 0.3173
## 80 18.6177 nan 0.0100 0.2361
## 100 14.6100 nan 0.0100 0.1648
## 120 11.6843 nan 0.0100 0.1067
## 140 9.5559 nan 0.0100 0.0870
## 160 8.0146 nan 0.0100 0.0602
## 180 6.8107 nan 0.0100 0.0493
## 200 5.9598 nan 0.0100 0.0210
## 220 5.3236 nan 0.0100 0.0226
## 240 4.7931 nan 0.0100 0.0190
## 260 4.4029 nan 0.0100 0.0096
## 280 4.1061 nan 0.0100 0.0102
## 300 3.8722 nan 0.0100 0.0061
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.8599 nan 0.0100 1.1045
## 2 59.7604 nan 0.0100 0.9577
## 3 58.7324 nan 0.0100 1.0759
## 4 57.6728 nan 0.0100 1.0633
## 5 56.6852 nan 0.0100 1.0514
## 6 55.6812 nan 0.0100 0.9947
## 7 54.6859 nan 0.0100 1.0101
## 8 53.7895 nan 0.0100 1.0009
## 9 52.9049 nan 0.0100 0.9661
## 10 52.0180 nan 0.0100 0.8686
## 20 43.7365 nan 0.0100 0.6808
## 40 31.1704 nan 0.0100 0.5059
## 60 22.6080 nan 0.0100 0.3243
## 80 16.7924 nan 0.0100 0.2327
## 100 12.6529 nan 0.0100 0.1739
## 120 9.8255 nan 0.0100 0.1081
## 140 7.8145 nan 0.0100 0.0805
## 160 6.3392 nan 0.0100 0.0546
## 180 5.2905 nan 0.0100 0.0444
## 200 4.5580 nan 0.0100 0.0262
## 220 4.0028 nan 0.0100 0.0108
## 240 3.5816 nan 0.0100 0.0081
## 260 3.2739 nan 0.0100 0.0070
## 280 3.0261 nan 0.0100 0.0037
## 300 2.8510 nan 0.0100 -0.0011
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.8466 nan 0.0100 1.1105
## 2 59.8173 nan 0.0100 1.0447
## 3 58.7741 nan 0.0100 0.9922
## 4 57.8056 nan 0.0100 1.0112
## 5 56.7986 nan 0.0100 0.9769
## 6 55.8752 nan 0.0100 0.9015
## 7 54.8514 nan 0.0100 1.0506
## 8 53.9511 nan 0.0100 0.9219
## 9 52.9724 nan 0.0100 0.9615
## 10 52.0419 nan 0.0100 0.9956
## 20 43.6749 nan 0.0100 0.8140
## 40 31.2287 nan 0.0100 0.5267
## 60 22.5799 nan 0.0100 0.3256
## 80 16.8578 nan 0.0100 0.2478
## 100 12.7729 nan 0.0100 0.1684
## 120 9.9485 nan 0.0100 0.1144
## 140 7.9676 nan 0.0100 0.0683
## 160 6.5100 nan 0.0100 0.0572
## 180 5.4399 nan 0.0100 0.0358
## 200 4.7002 nan 0.0100 0.0276
## 220 4.1695 nan 0.0100 0.0184
## 240 3.7746 nan 0.0100 0.0103
## 260 3.4784 nan 0.0100 0.0007
## 280 3.2470 nan 0.0100 0.0029
## 300 3.0648 nan 0.0100 0.0043
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.9114 nan 0.0100 1.0235
## 2 59.8544 nan 0.0100 1.0829
## 3 58.8256 nan 0.0100 1.0491
## 4 57.8158 nan 0.0100 1.0343
## 5 56.7871 nan 0.0100 1.0397
## 6 55.8776 nan 0.0100 0.9607
## 7 54.8405 nan 0.0100 0.9479
## 8 53.9008 nan 0.0100 1.0173
## 9 52.9865 nan 0.0100 0.8911
## 10 52.0564 nan 0.0100 0.9678
## 20 43.8467 nan 0.0100 0.7355
## 40 31.4376 nan 0.0100 0.5162
## 60 22.9465 nan 0.0100 0.3335
## 80 17.1166 nan 0.0100 0.2320
## 100 13.1458 nan 0.0100 0.1522
## 120 10.3169 nan 0.0100 0.1267
## 140 8.3525 nan 0.0100 0.0663
## 160 6.8938 nan 0.0100 0.0608
## 180 5.8778 nan 0.0100 0.0362
## 200 5.1397 nan 0.0100 0.0247
## 220 4.5938 nan 0.0100 0.0237
## 240 4.1886 nan 0.0100 0.0136
## 260 3.8909 nan 0.0100 0.0114
## 280 3.6706 nan 0.0100 0.0014
## 300 3.4952 nan 0.0100 0.0014
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.0320 nan 0.0500 3.9036
## 2 54.5431 nan 0.0500 3.1505
## 3 51.3175 nan 0.0500 3.3125
## 4 48.5021 nan 0.0500 2.9328
## 5 45.5377 nan 0.0500 2.7748
## 6 43.2551 nan 0.0500 2.1519
## 7 41.0764 nan 0.0500 2.0129
## 8 38.9532 nan 0.0500 2.1007
## 9 36.6735 nan 0.0500 1.9303
## 10 34.8733 nan 0.0500 1.7155
## 20 21.8982 nan 0.0500 0.9277
## 40 11.0476 nan 0.0500 0.2596
## 60 6.9322 nan 0.0500 0.0827
## 80 5.0555 nan 0.0500 0.0309
## 100 4.1734 nan 0.0500 0.0137
## 120 3.7203 nan 0.0500 0.0069
## 140 3.4903 nan 0.0500 -0.0055
## 160 3.3660 nan 0.0500 -0.0207
## 180 3.2785 nan 0.0500 -0.0133
## 200 3.2111 nan 0.0500 -0.0064
## 220 3.1337 nan 0.0500 -0.0177
## 240 3.0943 nan 0.0500 -0.0157
## 260 3.0177 nan 0.0500 -0.0039
## 280 2.9654 nan 0.0500 -0.0076
## 300 2.9303 nan 0.0500 -0.0175
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.8246 nan 0.0500 3.8069
## 2 54.5173 nan 0.0500 3.4161
## 3 51.5395 nan 0.0500 3.3087
## 4 48.6179 nan 0.0500 3.0133
## 5 45.9722 nan 0.0500 2.8870
## 6 43.6940 nan 0.0500 2.3218
## 7 41.3385 nan 0.0500 2.2202
## 8 39.2852 nan 0.0500 2.0523
## 9 37.3543 nan 0.0500 1.7968
## 10 35.5395 nan 0.0500 1.7426
## 20 21.9442 nan 0.0500 1.0094
## 40 11.0329 nan 0.0500 0.2508
## 60 6.8113 nan 0.0500 0.1251
## 80 5.0744 nan 0.0500 0.0393
## 100 4.2370 nan 0.0500 0.0108
## 120 3.8250 nan 0.0500 0.0046
## 140 3.6007 nan 0.0500 -0.0011
## 160 3.4859 nan 0.0500 -0.0121
## 180 3.3932 nan 0.0500 -0.0091
## 200 3.3100 nan 0.0500 -0.0063
## 220 3.2454 nan 0.0500 0.0011
## 240 3.1762 nan 0.0500 -0.0037
## 260 3.1160 nan 0.0500 -0.0041
## 280 3.0701 nan 0.0500 -0.0065
## 300 3.0351 nan 0.0500 -0.0096
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.0441 nan 0.0500 3.7926
## 2 54.8287 nan 0.0500 3.6888
## 3 51.5855 nan 0.0500 3.0969
## 4 48.2923 nan 0.0500 2.7331
## 5 45.6297 nan 0.0500 2.7493
## 6 43.1559 nan 0.0500 2.5933
## 7 40.9070 nan 0.0500 2.1149
## 8 38.5866 nan 0.0500 1.9367
## 9 36.6783 nan 0.0500 1.9797
## 10 35.0153 nan 0.0500 1.7954
## 20 22.1595 nan 0.0500 0.9428
## 40 11.1369 nan 0.0500 0.2178
## 60 7.1095 nan 0.0500 0.1208
## 80 5.2042 nan 0.0500 0.0616
## 100 4.3460 nan 0.0500 0.0219
## 120 3.9957 nan 0.0500 -0.0275
## 140 3.8210 nan 0.0500 -0.0185
## 160 3.6894 nan 0.0500 -0.0012
## 180 3.5919 nan 0.0500 -0.0037
## 200 3.5230 nan 0.0500 -0.0092
## 220 3.4410 nan 0.0500 -0.0068
## 240 3.3806 nan 0.0500 -0.0187
## 260 3.3166 nan 0.0500 -0.0033
## 280 3.2670 nan 0.0500 -0.0127
## 300 3.2187 nan 0.0500 -0.0120
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.9732 nan 0.0500 5.0766
## 2 52.4679 nan 0.0500 4.3311
## 3 48.3264 nan 0.0500 3.8133
## 4 44.4243 nan 0.0500 3.6729
## 5 40.9426 nan 0.0500 3.7253
## 6 37.8368 nan 0.0500 3.1277
## 7 34.8356 nan 0.0500 2.7551
## 8 32.2039 nan 0.0500 2.4053
## 9 29.7965 nan 0.0500 2.1979
## 10 27.6176 nan 0.0500 2.0831
## 20 14.1842 nan 0.0500 0.8042
## 40 5.7740 nan 0.0500 0.1591
## 60 3.5590 nan 0.0500 0.0044
## 80 2.9460 nan 0.0500 -0.0268
## 100 2.6220 nan 0.0500 -0.0039
## 120 2.3881 nan 0.0500 -0.0067
## 140 2.2174 nan 0.0500 -0.0256
## 160 2.0517 nan 0.0500 -0.0223
## 180 1.9344 nan 0.0500 -0.0076
## 200 1.8351 nan 0.0500 -0.0169
## 220 1.7387 nan 0.0500 -0.0091
## 240 1.6488 nan 0.0500 -0.0120
## 260 1.5618 nan 0.0500 -0.0094
## 280 1.4899 nan 0.0500 -0.0045
## 300 1.4252 nan 0.0500 -0.0073
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.7626 nan 0.0500 5.2616
## 2 52.2961 nan 0.0500 4.2161
## 3 48.1645 nan 0.0500 3.6381
## 4 44.2978 nan 0.0500 4.0835
## 5 40.6974 nan 0.0500 3.7189
## 6 37.5823 nan 0.0500 3.0162
## 7 34.7504 nan 0.0500 2.7777
## 8 32.1139 nan 0.0500 2.5326
## 9 29.6980 nan 0.0500 2.4592
## 10 27.4756 nan 0.0500 2.0332
## 20 14.0349 nan 0.0500 0.8806
## 40 5.5720 nan 0.0500 0.1319
## 60 3.5739 nan 0.0500 0.0133
## 80 3.0852 nan 0.0500 -0.0028
## 100 2.7889 nan 0.0500 -0.0090
## 120 2.5724 nan 0.0500 -0.0073
## 140 2.4002 nan 0.0500 -0.0085
## 160 2.2654 nan 0.0500 -0.0123
## 180 2.1400 nan 0.0500 -0.0147
## 200 2.0371 nan 0.0500 -0.0063
## 220 1.9388 nan 0.0500 -0.0162
## 240 1.8495 nan 0.0500 -0.0126
## 260 1.7852 nan 0.0500 -0.0208
## 280 1.7209 nan 0.0500 -0.0100
## 300 1.6538 nan 0.0500 -0.0078
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.9713 nan 0.0500 5.4928
## 2 52.4291 nan 0.0500 4.4699
## 3 48.3727 nan 0.0500 4.0367
## 4 44.4707 nan 0.0500 3.8570
## 5 40.9740 nan 0.0500 3.6530
## 6 37.9045 nan 0.0500 2.9720
## 7 34.8866 nan 0.0500 3.2349
## 8 32.2309 nan 0.0500 2.5453
## 9 29.7234 nan 0.0500 2.0863
## 10 27.7302 nan 0.0500 1.9942
## 20 14.3717 nan 0.0500 0.8875
## 40 5.9060 nan 0.0500 0.1763
## 60 3.9694 nan 0.0500 0.0145
## 80 3.4253 nan 0.0500 -0.0133
## 100 3.1380 nan 0.0500 -0.0205
## 120 2.9462 nan 0.0500 -0.0122
## 140 2.7932 nan 0.0500 -0.0273
## 160 2.6559 nan 0.0500 -0.0077
## 180 2.5309 nan 0.0500 -0.0160
## 200 2.4088 nan 0.0500 -0.0144
## 220 2.3305 nan 0.0500 -0.0130
## 240 2.2463 nan 0.0500 -0.0256
## 260 2.1812 nan 0.0500 -0.0085
## 280 2.0929 nan 0.0500 -0.0079
## 300 2.0246 nan 0.0500 -0.0124
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.6527 nan 0.0500 5.4209
## 2 51.3889 nan 0.0500 5.3058
## 3 46.8690 nan 0.0500 4.0886
## 4 42.9162 nan 0.0500 3.8024
## 5 39.3671 nan 0.0500 3.4322
## 6 36.2320 nan 0.0500 3.2942
## 7 33.1884 nan 0.0500 3.0409
## 8 30.5938 nan 0.0500 2.5090
## 9 28.1127 nan 0.0500 2.5185
## 10 25.9688 nan 0.0500 2.2446
## 20 12.3933 nan 0.0500 0.7846
## 40 4.4251 nan 0.0500 0.1171
## 60 2.7606 nan 0.0500 -0.0043
## 80 2.2441 nan 0.0500 -0.0061
## 100 1.9671 nan 0.0500 -0.0125
## 120 1.7889 nan 0.0500 -0.0104
## 140 1.6247 nan 0.0500 -0.0122
## 160 1.4880 nan 0.0500 -0.0014
## 180 1.3664 nan 0.0500 -0.0096
## 200 1.2483 nan 0.0500 -0.0045
## 220 1.1622 nan 0.0500 -0.0060
## 240 1.0733 nan 0.0500 -0.0074
## 260 0.9910 nan 0.0500 -0.0068
## 280 0.9281 nan 0.0500 -0.0112
## 300 0.8563 nan 0.0500 -0.0029
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.7463 nan 0.0500 5.5604
## 2 51.8746 nan 0.0500 4.5910
## 3 47.3341 nan 0.0500 4.7281
## 4 43.3017 nan 0.0500 4.4060
## 5 39.7371 nan 0.0500 3.3006
## 6 36.7604 nan 0.0500 3.1659
## 7 33.6199 nan 0.0500 2.5288
## 8 30.9942 nan 0.0500 2.6074
## 9 28.5158 nan 0.0500 2.3772
## 10 26.3997 nan 0.0500 2.1882
## 20 12.6284 nan 0.0500 0.8531
## 40 4.6844 nan 0.0500 0.1644
## 60 3.0655 nan 0.0500 0.0135
## 80 2.5299 nan 0.0500 -0.0102
## 100 2.2663 nan 0.0500 -0.0152
## 120 2.0987 nan 0.0500 -0.0134
## 140 1.9400 nan 0.0500 -0.0008
## 160 1.8088 nan 0.0500 -0.0139
## 180 1.7125 nan 0.0500 -0.0149
## 200 1.5990 nan 0.0500 -0.0219
## 220 1.4907 nan 0.0500 -0.0180
## 240 1.4066 nan 0.0500 -0.0061
## 260 1.3280 nan 0.0500 -0.0065
## 280 1.2644 nan 0.0500 -0.0080
## 300 1.1963 nan 0.0500 -0.0154
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.4938 nan 0.0500 5.2998
## 2 51.8435 nan 0.0500 4.9099
## 3 47.4414 nan 0.0500 3.9309
## 4 43.3504 nan 0.0500 4.2734
## 5 39.7379 nan 0.0500 3.3469
## 6 36.7073 nan 0.0500 3.2462
## 7 33.6378 nan 0.0500 2.9720
## 8 30.9603 nan 0.0500 2.8667
## 9 28.5738 nan 0.0500 2.4097
## 10 26.2968 nan 0.0500 2.2067
## 20 12.8494 nan 0.0500 0.9390
## 40 4.9731 nan 0.0500 0.0948
## 60 3.4319 nan 0.0500 0.0035
## 80 2.9577 nan 0.0500 -0.0232
## 100 2.7100 nan 0.0500 -0.0110
## 120 2.5209 nan 0.0500 -0.0142
## 140 2.3386 nan 0.0500 -0.0088
## 160 2.2338 nan 0.0500 -0.0102
## 180 2.1042 nan 0.0500 -0.0194
## 200 1.9966 nan 0.0500 -0.0109
## 220 1.8962 nan 0.0500 -0.0032
## 240 1.8105 nan 0.0500 -0.0221
## 260 1.7334 nan 0.0500 -0.0075
## 280 1.6411 nan 0.0500 -0.0117
## 300 1.5873 nan 0.0500 -0.0104
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.1977 nan 0.1000 7.4660
## 2 48.2178 nan 0.1000 5.9712
## 3 43.1700 nan 0.1000 4.1692
## 4 38.3877 nan 0.1000 4.4018
## 5 33.9463 nan 0.1000 3.7847
## 6 30.6617 nan 0.1000 2.9239
## 7 27.9505 nan 0.1000 2.6399
## 8 25.3583 nan 0.1000 2.8621
## 9 22.9037 nan 0.1000 2.1542
## 10 20.8359 nan 0.1000 1.9039
## 20 11.0221 nan 0.1000 0.7067
## 40 5.0339 nan 0.1000 0.0895
## 60 3.7857 nan 0.1000 0.0198
## 80 3.4479 nan 0.1000 -0.0242
## 100 3.2798 nan 0.1000 -0.0107
## 120 3.1685 nan 0.1000 -0.0306
## 140 3.0644 nan 0.1000 -0.0347
## 160 2.9826 nan 0.1000 -0.0131
## 180 2.9169 nan 0.1000 -0.0179
## 200 2.8581 nan 0.1000 -0.0097
## 220 2.7914 nan 0.1000 -0.0086
## 240 2.7264 nan 0.1000 -0.0089
## 260 2.6905 nan 0.1000 -0.0141
## 280 2.6638 nan 0.1000 -0.0289
## 300 2.5957 nan 0.1000 -0.0248
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.3415 nan 0.1000 7.1067
## 2 48.5181 nan 0.1000 6.2958
## 3 43.2629 nan 0.1000 5.1808
## 4 38.8502 nan 0.1000 4.2454
## 5 35.0131 nan 0.1000 3.6627
## 6 31.6117 nan 0.1000 3.3416
## 7 28.3329 nan 0.1000 2.9352
## 8 25.5805 nan 0.1000 2.5954
## 9 23.3408 nan 0.1000 1.9313
## 10 21.5298 nan 0.1000 1.7782
## 20 10.9913 nan 0.1000 0.4158
## 40 5.1504 nan 0.1000 0.1276
## 60 3.9973 nan 0.1000 0.0090
## 80 3.6917 nan 0.1000 -0.0281
## 100 3.5124 nan 0.1000 -0.0038
## 120 3.3691 nan 0.1000 -0.0213
## 140 3.2594 nan 0.1000 -0.0156
## 160 3.1812 nan 0.1000 -0.0512
## 180 3.0930 nan 0.1000 -0.0121
## 200 3.0201 nan 0.1000 -0.0090
## 220 2.9672 nan 0.1000 -0.0123
## 240 2.9189 nan 0.1000 -0.0117
## 260 2.8583 nan 0.1000 -0.0131
## 280 2.8068 nan 0.1000 -0.0132
## 300 2.7506 nan 0.1000 -0.0132
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.5799 nan 0.1000 7.5566
## 2 48.5670 nan 0.1000 6.2876
## 3 43.3600 nan 0.1000 5.2236
## 4 38.5741 nan 0.1000 4.5952
## 5 34.6908 nan 0.1000 3.5599
## 6 31.3011 nan 0.1000 3.0332
## 7 28.4832 nan 0.1000 2.7306
## 8 25.6532 nan 0.1000 2.6216
## 9 23.4697 nan 0.1000 2.0832
## 10 21.4403 nan 0.1000 1.6425
## 20 11.0070 nan 0.1000 0.4945
## 40 5.2678 nan 0.1000 0.0732
## 60 4.0230 nan 0.1000 0.0062
## 80 3.6998 nan 0.1000 -0.0283
## 100 3.5201 nan 0.1000 -0.0098
## 120 3.3967 nan 0.1000 -0.0081
## 140 3.2490 nan 0.1000 -0.0474
## 160 3.1703 nan 0.1000 -0.0180
## 180 3.1149 nan 0.1000 -0.0271
## 200 3.0444 nan 0.1000 -0.0383
## 220 2.9657 nan 0.1000 -0.0251
## 240 2.8947 nan 0.1000 -0.0369
## 260 2.8454 nan 0.1000 -0.0090
## 280 2.8107 nan 0.1000 -0.0119
## 300 2.7774 nan 0.1000 -0.0130
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.1763 nan 0.1000 10.3046
## 2 44.2383 nan 0.1000 8.2328
## 3 37.3153 nan 0.1000 6.3583
## 4 31.9485 nan 0.1000 5.5184
## 5 27.4657 nan 0.1000 3.9867
## 6 23.3813 nan 0.1000 3.5071
## 7 20.1588 nan 0.1000 3.0212
## 8 17.6364 nan 0.1000 2.6216
## 9 15.5000 nan 0.1000 2.0555
## 10 13.6467 nan 0.1000 1.5339
## 20 5.3586 nan 0.1000 0.3444
## 40 2.9241 nan 0.1000 -0.0112
## 60 2.3350 nan 0.1000 -0.0110
## 80 2.0820 nan 0.1000 -0.0170
## 100 1.8367 nan 0.1000 -0.0142
## 120 1.6791 nan 0.1000 -0.0313
## 140 1.5618 nan 0.1000 -0.0356
## 160 1.4122 nan 0.1000 -0.0147
## 180 1.3137 nan 0.1000 -0.0235
## 200 1.2206 nan 0.1000 -0.0130
## 220 1.1456 nan 0.1000 -0.0122
## 240 1.0858 nan 0.1000 -0.0128
## 260 1.0233 nan 0.1000 -0.0046
## 280 0.9600 nan 0.1000 -0.0083
## 300 0.8859 nan 0.1000 -0.0042
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.3655 nan 0.1000 9.5434
## 2 44.3622 nan 0.1000 7.3696
## 3 38.0453 nan 0.1000 5.7417
## 4 32.6289 nan 0.1000 6.1526
## 5 27.6899 nan 0.1000 4.2952
## 6 23.8558 nan 0.1000 3.8494
## 7 20.4550 nan 0.1000 3.5154
## 8 17.9055 nan 0.1000 2.5476
## 9 15.6375 nan 0.1000 2.0716
## 10 13.9656 nan 0.1000 1.6323
## 20 5.5211 nan 0.1000 0.2734
## 40 3.2364 nan 0.1000 -0.0001
## 60 2.6540 nan 0.1000 0.0034
## 80 2.3800 nan 0.1000 -0.0281
## 100 2.1544 nan 0.1000 -0.0049
## 120 1.9728 nan 0.1000 -0.0159
## 140 1.8236 nan 0.1000 -0.0473
## 160 1.7040 nan 0.1000 -0.0176
## 180 1.6045 nan 0.1000 -0.0202
## 200 1.4994 nan 0.1000 -0.0253
## 220 1.3982 nan 0.1000 -0.0162
## 240 1.3133 nan 0.1000 -0.0178
## 260 1.2456 nan 0.1000 -0.0111
## 280 1.1974 nan 0.1000 -0.0143
## 300 1.1411 nan 0.1000 -0.0130
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.4274 nan 0.1000 10.1398
## 2 44.4224 nan 0.1000 8.2069
## 3 38.0346 nan 0.1000 6.8476
## 4 32.5190 nan 0.1000 4.8989
## 5 27.8687 nan 0.1000 3.9048
## 6 24.2671 nan 0.1000 3.4605
## 7 20.8978 nan 0.1000 3.1609
## 8 18.2995 nan 0.1000 2.5340
## 9 16.2640 nan 0.1000 1.8258
## 10 14.3728 nan 0.1000 1.8466
## 20 5.8116 nan 0.1000 0.2398
## 40 3.4061 nan 0.1000 -0.0041
## 60 2.9419 nan 0.1000 -0.0363
## 80 2.6451 nan 0.1000 -0.0209
## 100 2.4001 nan 0.1000 -0.0230
## 120 2.2060 nan 0.1000 -0.0294
## 140 2.0125 nan 0.1000 -0.0084
## 160 1.8804 nan 0.1000 -0.0183
## 180 1.7790 nan 0.1000 -0.0200
## 200 1.6883 nan 0.1000 -0.0227
## 220 1.5905 nan 0.1000 -0.0286
## 240 1.5090 nan 0.1000 -0.0141
## 260 1.4484 nan 0.1000 -0.0111
## 280 1.3860 nan 0.1000 -0.0195
## 300 1.3285 nan 0.1000 -0.0124
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.6338 nan 0.1000 10.6310
## 2 43.1409 nan 0.1000 7.8526
## 3 35.8119 nan 0.1000 6.9085
## 4 30.2242 nan 0.1000 5.5063
## 5 25.1533 nan 0.1000 5.0691
## 6 21.3656 nan 0.1000 3.7887
## 7 18.2957 nan 0.1000 2.9007
## 8 15.7961 nan 0.1000 2.3271
## 9 13.6561 nan 0.1000 2.1348
## 10 11.8394 nan 0.1000 1.6803
## 20 4.3106 nan 0.1000 0.2317
## 40 2.3415 nan 0.1000 -0.0308
## 60 1.8198 nan 0.1000 -0.0168
## 80 1.5360 nan 0.1000 -0.0123
## 100 1.2859 nan 0.1000 -0.0163
## 120 1.0852 nan 0.1000 -0.0188
## 140 0.9553 nan 0.1000 -0.0140
## 160 0.8472 nan 0.1000 -0.0142
## 180 0.7443 nan 0.1000 -0.0144
## 200 0.6633 nan 0.1000 -0.0096
## 220 0.5865 nan 0.1000 -0.0075
## 240 0.5197 nan 0.1000 -0.0067
## 260 0.4666 nan 0.1000 -0.0147
## 280 0.4194 nan 0.1000 -0.0104
## 300 0.3754 nan 0.1000 -0.0051
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.2678 nan 0.1000 10.9446
## 2 42.2272 nan 0.1000 9.5996
## 3 35.6808 nan 0.1000 6.7896
## 4 30.1885 nan 0.1000 5.5910
## 5 25.5106 nan 0.1000 4.6908
## 6 21.6233 nan 0.1000 4.2761
## 7 18.3163 nan 0.1000 3.0600
## 8 15.5537 nan 0.1000 2.3474
## 9 13.7931 nan 0.1000 1.9919
## 10 12.1228 nan 0.1000 1.6952
## 20 4.6926 nan 0.1000 0.1830
## 40 2.7273 nan 0.1000 -0.0374
## 60 2.2169 nan 0.1000 -0.0309
## 80 1.9304 nan 0.1000 -0.0521
## 100 1.7005 nan 0.1000 -0.0588
## 120 1.5040 nan 0.1000 -0.0177
## 140 1.3514 nan 0.1000 -0.0204
## 160 1.2131 nan 0.1000 -0.0137
## 180 1.0999 nan 0.1000 -0.0234
## 200 0.9947 nan 0.1000 -0.0285
## 220 0.8928 nan 0.1000 -0.0045
## 240 0.8237 nan 0.1000 -0.0141
## 260 0.7514 nan 0.1000 -0.0229
## 280 0.6977 nan 0.1000 -0.0150
## 300 0.6397 nan 0.1000 -0.0051
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.5243 nan 0.1000 10.3151
## 2 43.5037 nan 0.1000 8.0391
## 3 36.3146 nan 0.1000 6.2526
## 4 30.5839 nan 0.1000 5.7645
## 5 25.9360 nan 0.1000 3.9629
## 6 22.1256 nan 0.1000 3.4614
## 7 18.9333 nan 0.1000 2.8435
## 8 16.1183 nan 0.1000 2.3615
## 9 13.7670 nan 0.1000 2.0181
## 10 12.1843 nan 0.1000 1.6366
## 20 4.7071 nan 0.1000 0.2993
## 40 3.0248 nan 0.1000 -0.0216
## 60 2.5585 nan 0.1000 -0.0063
## 80 2.2927 nan 0.1000 -0.0548
## 100 2.0388 nan 0.1000 -0.0346
## 120 1.8565 nan 0.1000 -0.0123
## 140 1.7084 nan 0.1000 -0.0177
## 160 1.6002 nan 0.1000 -0.0267
## 180 1.4884 nan 0.1000 -0.0169
## 200 1.3894 nan 0.1000 -0.0094
## 220 1.3004 nan 0.1000 -0.0077
## 240 1.2206 nan 0.1000 -0.0155
## 260 1.1393 nan 0.1000 -0.0153
## 280 1.0749 nan 0.1000 -0.0144
## 300 1.0164 nan 0.1000 -0.0108
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.5310 nan 0.0100 0.7276
## 2 58.7274 nan 0.0100 0.7540
## 3 58.0097 nan 0.0100 0.7208
## 4 57.2996 nan 0.0100 0.6594
## 5 56.5967 nan 0.0100 0.6803
## 6 55.9249 nan 0.0100 0.6456
## 7 55.2071 nan 0.0100 0.6612
## 8 54.5822 nan 0.0100 0.6166
## 9 53.9591 nan 0.0100 0.6489
## 10 53.3008 nan 0.0100 0.6375
## 20 47.3029 nan 0.0100 0.5035
## 40 38.1055 nan 0.0100 0.3775
## 60 31.2738 nan 0.0100 0.2812
## 80 26.0247 nan 0.0100 0.2264
## 100 21.9756 nan 0.0100 0.1770
## 120 18.8576 nan 0.0100 0.0893
## 140 16.4067 nan 0.0100 0.0491
## 160 14.3861 nan 0.0100 0.0727
## 180 12.7174 nan 0.0100 0.0818
## 200 11.3596 nan 0.0100 0.0522
## 220 10.2164 nan 0.0100 0.0333
## 240 9.2647 nan 0.0100 0.0190
## 260 8.4918 nan 0.0100 0.0308
## 280 7.8139 nan 0.0100 0.0234
## 300 7.2264 nan 0.0100 0.0205
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.4608 nan 0.0100 0.7181
## 2 58.6655 nan 0.0100 0.7044
## 3 57.9136 nan 0.0100 0.7424
## 4 57.2325 nan 0.0100 0.7134
## 5 56.5630 nan 0.0100 0.7258
## 6 55.8832 nan 0.0100 0.6459
## 7 55.2360 nan 0.0100 0.6986
## 8 54.6031 nan 0.0100 0.6654
## 9 53.9255 nan 0.0100 0.6405
## 10 53.2681 nan 0.0100 0.6429
## 20 47.4623 nan 0.0100 0.5270
## 40 38.4321 nan 0.0100 0.3985
## 60 31.5295 nan 0.0100 0.3064
## 80 26.2660 nan 0.0100 0.2518
## 100 22.1788 nan 0.0100 0.1801
## 120 18.9704 nan 0.0100 0.0960
## 140 16.4751 nan 0.0100 0.0947
## 160 14.4683 nan 0.0100 0.0766
## 180 12.7725 nan 0.0100 0.0624
## 200 11.4226 nan 0.0100 0.0514
## 220 10.3344 nan 0.0100 0.0391
## 240 9.3668 nan 0.0100 0.0276
## 260 8.5621 nan 0.0100 0.0299
## 280 7.8826 nan 0.0100 0.0246
## 300 7.2998 nan 0.0100 0.0163
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.4931 nan 0.0100 0.7427
## 2 58.7447 nan 0.0100 0.7792
## 3 58.0285 nan 0.0100 0.7712
## 4 57.3424 nan 0.0100 0.7535
## 5 56.6934 nan 0.0100 0.6807
## 6 56.0248 nan 0.0100 0.6733
## 7 55.3520 nan 0.0100 0.6468
## 8 54.6651 nan 0.0100 0.6637
## 9 54.0678 nan 0.0100 0.6154
## 10 53.5092 nan 0.0100 0.6202
## 20 47.6536 nan 0.0100 0.4539
## 40 38.5259 nan 0.0100 0.3714
## 60 31.5008 nan 0.0100 0.2844
## 80 26.2746 nan 0.0100 0.2227
## 100 22.0520 nan 0.0100 0.1755
## 120 18.9481 nan 0.0100 0.1335
## 140 16.5071 nan 0.0100 0.1044
## 160 14.4742 nan 0.0100 0.0697
## 180 12.8642 nan 0.0100 0.0716
## 200 11.5470 nan 0.0100 0.0503
## 220 10.4247 nan 0.0100 0.0510
## 240 9.5027 nan 0.0100 0.0409
## 260 8.7361 nan 0.0100 0.0266
## 280 8.0682 nan 0.0100 0.0226
## 300 7.4985 nan 0.0100 0.0252
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.2562 nan 0.0100 0.9495
## 2 58.2776 nan 0.0100 0.8652
## 3 57.2564 nan 0.0100 0.9598
## 4 56.2957 nan 0.0100 0.9446
## 5 55.3454 nan 0.0100 0.8770
## 6 54.4817 nan 0.0100 0.9847
## 7 53.5707 nan 0.0100 0.8120
## 8 52.7068 nan 0.0100 0.9104
## 9 51.8652 nan 0.0100 0.8198
## 10 50.9812 nan 0.0100 0.8332
## 20 43.3049 nan 0.0100 0.6795
## 40 31.8561 nan 0.0100 0.4527
## 60 23.8294 nan 0.0100 0.2563
## 80 18.3028 nan 0.0100 0.1789
## 100 14.3420 nan 0.0100 0.1493
## 120 11.5584 nan 0.0100 0.1105
## 140 9.3850 nan 0.0100 0.0914
## 160 7.8056 nan 0.0100 0.0626
## 180 6.6584 nan 0.0100 0.0417
## 200 5.7627 nan 0.0100 0.0294
## 220 5.1003 nan 0.0100 0.0192
## 240 4.5476 nan 0.0100 0.0150
## 260 4.1614 nan 0.0100 0.0095
## 280 3.8294 nan 0.0100 0.0100
## 300 3.5996 nan 0.0100 0.0057
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.2168 nan 0.0100 0.8972
## 2 58.2118 nan 0.0100 1.0036
## 3 57.2523 nan 0.0100 0.9117
## 4 56.3160 nan 0.0100 1.0042
## 5 55.3696 nan 0.0100 0.8439
## 6 54.4433 nan 0.0100 0.7690
## 7 53.5090 nan 0.0100 0.8167
## 8 52.6270 nan 0.0100 0.7579
## 9 51.8101 nan 0.0100 0.8046
## 10 50.9744 nan 0.0100 0.8645
## 20 43.2930 nan 0.0100 0.7352
## 40 31.9709 nan 0.0100 0.4822
## 60 23.9414 nan 0.0100 0.3245
## 80 18.3799 nan 0.0100 0.2293
## 100 14.4788 nan 0.0100 0.1678
## 120 11.6612 nan 0.0100 0.0833
## 140 9.5193 nan 0.0100 0.0848
## 160 7.8943 nan 0.0100 0.0562
## 180 6.7135 nan 0.0100 0.0483
## 200 5.8479 nan 0.0100 0.0352
## 220 5.1854 nan 0.0100 0.0230
## 240 4.6985 nan 0.0100 0.0126
## 260 4.3008 nan 0.0100 0.0111
## 280 4.0089 nan 0.0100 0.0041
## 300 3.7798 nan 0.0100 0.0073
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.2672 nan 0.0100 1.0476
## 2 58.2366 nan 0.0100 1.0227
## 3 57.2547 nan 0.0100 0.8995
## 4 56.2928 nan 0.0100 0.8597
## 5 55.3744 nan 0.0100 0.8448
## 6 54.4875 nan 0.0100 0.8610
## 7 53.6227 nan 0.0100 0.8626
## 8 52.7994 nan 0.0100 0.8165
## 9 51.9697 nan 0.0100 0.8049
## 10 51.1370 nan 0.0100 0.7252
## 20 43.4795 nan 0.0100 0.7595
## 40 32.0588 nan 0.0100 0.4886
## 60 24.0832 nan 0.0100 0.3181
## 80 18.5443 nan 0.0100 0.1998
## 100 14.6046 nan 0.0100 0.1416
## 120 11.8787 nan 0.0100 0.0990
## 140 9.7991 nan 0.0100 0.0829
## 160 8.2642 nan 0.0100 0.0593
## 180 7.0519 nan 0.0100 0.0470
## 200 6.1789 nan 0.0100 0.0273
## 220 5.4887 nan 0.0100 0.0203
## 240 4.9829 nan 0.0100 0.0135
## 260 4.6013 nan 0.0100 0.0079
## 280 4.2946 nan 0.0100 0.0088
## 300 4.0803 nan 0.0100 0.0046
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.1660 nan 0.0100 1.0533
## 2 58.1259 nan 0.0100 1.1299
## 3 57.0723 nan 0.0100 1.1273
## 4 56.0621 nan 0.0100 0.8128
## 5 55.0832 nan 0.0100 0.8950
## 6 54.1275 nan 0.0100 0.9705
## 7 53.2139 nan 0.0100 0.8460
## 8 52.2474 nan 0.0100 0.8900
## 9 51.3724 nan 0.0100 0.9093
## 10 50.5048 nan 0.0100 0.9553
## 20 42.4873 nan 0.0100 0.7225
## 40 30.4558 nan 0.0100 0.4640
## 60 22.1976 nan 0.0100 0.3334
## 80 16.3987 nan 0.0100 0.2039
## 100 12.4337 nan 0.0100 0.1314
## 120 9.7016 nan 0.0100 0.1237
## 140 7.7292 nan 0.0100 0.0744
## 160 6.3353 nan 0.0100 0.0539
## 180 5.3284 nan 0.0100 0.0429
## 200 4.5739 nan 0.0100 0.0261
## 220 4.0087 nan 0.0100 0.0227
## 240 3.5926 nan 0.0100 0.0081
## 260 3.2951 nan 0.0100 0.0033
## 280 3.0562 nan 0.0100 0.0021
## 300 2.8713 nan 0.0100 0.0046
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.1759 nan 0.0100 1.0092
## 2 58.1542 nan 0.0100 1.0883
## 3 57.1099 nan 0.0100 1.0436
## 4 56.1356 nan 0.0100 0.9374
## 5 55.1770 nan 0.0100 0.8702
## 6 54.2092 nan 0.0100 1.0263
## 7 53.3252 nan 0.0100 0.9006
## 8 52.4076 nan 0.0100 0.9524
## 9 51.5204 nan 0.0100 0.8619
## 10 50.6685 nan 0.0100 0.8119
## 20 42.6965 nan 0.0100 0.7701
## 40 30.5920 nan 0.0100 0.5110
## 60 22.3897 nan 0.0100 0.3454
## 80 16.7214 nan 0.0100 0.2645
## 100 12.6832 nan 0.0100 0.1565
## 120 9.8446 nan 0.0100 0.1136
## 140 7.8585 nan 0.0100 0.0836
## 160 6.4842 nan 0.0100 0.0524
## 180 5.4501 nan 0.0100 0.0426
## 200 4.7094 nan 0.0100 0.0240
## 220 4.1711 nan 0.0100 0.0131
## 240 3.7629 nan 0.0100 0.0041
## 260 3.4784 nan 0.0100 0.0064
## 280 3.2599 nan 0.0100 0.0016
## 300 3.0861 nan 0.0100 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.1830 nan 0.0100 0.9324
## 2 58.1254 nan 0.0100 0.9812
## 3 57.1255 nan 0.0100 0.9642
## 4 56.1621 nan 0.0100 0.9591
## 5 55.2049 nan 0.0100 0.8737
## 6 54.2480 nan 0.0100 0.9627
## 7 53.3274 nan 0.0100 0.8895
## 8 52.4552 nan 0.0100 0.8572
## 9 51.5569 nan 0.0100 0.8378
## 10 50.6552 nan 0.0100 0.8811
## 20 42.6543 nan 0.0100 0.7007
## 40 30.7191 nan 0.0100 0.4778
## 60 22.5604 nan 0.0100 0.3193
## 80 16.8728 nan 0.0100 0.2143
## 100 12.9632 nan 0.0100 0.1605
## 120 10.1685 nan 0.0100 0.1139
## 140 8.2640 nan 0.0100 0.0662
## 160 6.9085 nan 0.0100 0.0561
## 180 5.8667 nan 0.0100 0.0390
## 200 5.1421 nan 0.0100 0.0260
## 220 4.6250 nan 0.0100 0.0159
## 240 4.2565 nan 0.0100 0.0059
## 260 3.9693 nan 0.0100 0.0150
## 280 3.7530 nan 0.0100 0.0036
## 300 3.5810 nan 0.0100 0.0023
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.2172 nan 0.0500 3.5345
## 2 53.0580 nan 0.0500 3.3131
## 3 50.0289 nan 0.0500 3.1580
## 4 47.3257 nan 0.0500 2.4752
## 5 44.7458 nan 0.0500 2.2671
## 6 42.1244 nan 0.0500 2.3969
## 7 39.9029 nan 0.0500 2.2012
## 8 38.0133 nan 0.0500 1.8091
## 9 36.1339 nan 0.0500 1.7790
## 10 34.3214 nan 0.0500 1.8085
## 20 21.9802 nan 0.0500 0.7899
## 40 11.5176 nan 0.0500 0.2089
## 60 7.2705 nan 0.0500 0.0793
## 80 5.3203 nan 0.0500 0.0324
## 100 4.4005 nan 0.0500 0.0020
## 120 3.8871 nan 0.0500 -0.0012
## 140 3.6344 nan 0.0500 0.0057
## 160 3.4908 nan 0.0500 -0.0004
## 180 3.3927 nan 0.0500 -0.0053
## 200 3.2918 nan 0.0500 -0.0019
## 220 3.2162 nan 0.0500 -0.0043
## 240 3.1590 nan 0.0500 -0.0041
## 260 3.0991 nan 0.0500 -0.0087
## 280 3.0532 nan 0.0500 -0.0022
## 300 3.0042 nan 0.0500 -0.0011
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.5490 nan 0.0500 3.4438
## 2 53.2862 nan 0.0500 3.4542
## 3 50.4501 nan 0.0500 2.4023
## 4 47.5761 nan 0.0500 2.8047
## 5 44.8229 nan 0.0500 2.5701
## 6 42.2079 nan 0.0500 2.3402
## 7 39.6999 nan 0.0500 2.3014
## 8 37.4855 nan 0.0500 1.9654
## 9 35.5773 nan 0.0500 1.9692
## 10 33.7154 nan 0.0500 1.6105
## 20 21.7557 nan 0.0500 0.8792
## 40 11.3053 nan 0.0500 0.2809
## 60 7.4102 nan 0.0500 0.0932
## 80 5.5168 nan 0.0500 0.0281
## 100 4.6080 nan 0.0500 -0.0069
## 120 4.1156 nan 0.0500 0.0085
## 140 3.8375 nan 0.0500 0.0029
## 160 3.6796 nan 0.0500 -0.0032
## 180 3.5564 nan 0.0500 -0.0047
## 200 3.4547 nan 0.0500 -0.0022
## 220 3.3867 nan 0.0500 -0.0022
## 240 3.3141 nan 0.0500 -0.0023
## 260 3.2498 nan 0.0500 -0.0153
## 280 3.1983 nan 0.0500 -0.0067
## 300 3.1387 nan 0.0500 -0.0061
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.4512 nan 0.0500 3.8397
## 2 53.1691 nan 0.0500 3.3514
## 3 50.0897 nan 0.0500 3.0425
## 4 47.3378 nan 0.0500 2.4967
## 5 44.6938 nan 0.0500 2.4810
## 6 42.4108 nan 0.0500 2.4061
## 7 40.1914 nan 0.0500 1.8212
## 8 38.1419 nan 0.0500 1.9198
## 9 36.3059 nan 0.0500 1.8363
## 10 34.4816 nan 0.0500 1.4315
## 20 21.7611 nan 0.0500 0.9200
## 40 11.2906 nan 0.0500 0.2416
## 60 7.3961 nan 0.0500 0.1041
## 80 5.5350 nan 0.0500 0.0360
## 100 4.6021 nan 0.0500 0.0282
## 120 4.1641 nan 0.0500 0.0127
## 140 3.9733 nan 0.0500 -0.0040
## 160 3.8452 nan 0.0500 0.0015
## 180 3.7473 nan 0.0500 0.0054
## 200 3.6667 nan 0.0500 -0.0151
## 220 3.5911 nan 0.0500 -0.0088
## 240 3.5337 nan 0.0500 -0.0004
## 260 3.4694 nan 0.0500 -0.0093
## 280 3.4218 nan 0.0500 -0.0064
## 300 3.3818 nan 0.0500 -0.0004
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.3105 nan 0.0500 4.6992
## 2 50.8135 nan 0.0500 4.9273
## 3 46.7715 nan 0.0500 3.6046
## 4 43.2418 nan 0.0500 3.4671
## 5 39.8614 nan 0.0500 2.9666
## 6 36.7503 nan 0.0500 2.6636
## 7 34.0464 nan 0.0500 2.6040
## 8 31.5051 nan 0.0500 2.5223
## 9 29.1072 nan 0.0500 2.3144
## 10 26.9775 nan 0.0500 1.7371
## 20 13.9623 nan 0.0500 0.8471
## 40 5.7412 nan 0.0500 0.1827
## 60 3.6319 nan 0.0500 0.0399
## 80 2.9357 nan 0.0500 0.0072
## 100 2.5925 nan 0.0500 -0.0149
## 120 2.3684 nan 0.0500 -0.0146
## 140 2.2093 nan 0.0500 -0.0125
## 160 2.0621 nan 0.0500 -0.0024
## 180 1.9443 nan 0.0500 -0.0153
## 200 1.8580 nan 0.0500 -0.0146
## 220 1.7489 nan 0.0500 -0.0072
## 240 1.6616 nan 0.0500 -0.0111
## 260 1.6007 nan 0.0500 -0.0121
## 280 1.5365 nan 0.0500 -0.0064
## 300 1.4736 nan 0.0500 -0.0054
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.3460 nan 0.0500 4.8082
## 2 51.1024 nan 0.0500 4.1978
## 3 46.9398 nan 0.0500 3.9122
## 4 43.1605 nan 0.0500 3.4750
## 5 39.6809 nan 0.0500 3.5362
## 6 36.7364 nan 0.0500 2.6174
## 7 34.0597 nan 0.0500 2.5813
## 8 31.5075 nan 0.0500 2.3642
## 9 29.3071 nan 0.0500 2.1936
## 10 27.2265 nan 0.0500 1.8889
## 20 14.0143 nan 0.0500 0.8055
## 40 5.7156 nan 0.0500 0.1698
## 60 3.7682 nan 0.0500 0.0161
## 80 3.1659 nan 0.0500 -0.0168
## 100 2.8759 nan 0.0500 0.0044
## 120 2.6875 nan 0.0500 -0.0132
## 140 2.5153 nan 0.0500 -0.0075
## 160 2.3933 nan 0.0500 -0.0151
## 180 2.2462 nan 0.0500 -0.0121
## 200 2.1533 nan 0.0500 -0.0092
## 220 2.0818 nan 0.0500 -0.0031
## 240 1.9915 nan 0.0500 -0.0122
## 260 1.9341 nan 0.0500 -0.0136
## 280 1.8615 nan 0.0500 -0.0121
## 300 1.7806 nan 0.0500 -0.0056
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.4673 nan 0.0500 4.8546
## 2 51.0004 nan 0.0500 4.7062
## 3 46.8684 nan 0.0500 3.8162
## 4 43.1961 nan 0.0500 3.6055
## 5 39.7377 nan 0.0500 3.2091
## 6 36.9828 nan 0.0500 2.8802
## 7 34.2291 nan 0.0500 2.8777
## 8 31.6605 nan 0.0500 2.1394
## 9 29.4957 nan 0.0500 2.4222
## 10 27.3567 nan 0.0500 2.0692
## 20 14.2855 nan 0.0500 0.6694
## 40 6.1202 nan 0.0500 0.1544
## 60 4.1146 nan 0.0500 0.0206
## 80 3.4638 nan 0.0500 -0.0198
## 100 3.1951 nan 0.0500 -0.0115
## 120 3.0209 nan 0.0500 -0.0064
## 140 2.8661 nan 0.0500 -0.0085
## 160 2.7551 nan 0.0500 -0.0093
## 180 2.6418 nan 0.0500 -0.0082
## 200 2.5480 nan 0.0500 -0.0204
## 220 2.4556 nan 0.0500 -0.0033
## 240 2.3835 nan 0.0500 -0.0134
## 260 2.3073 nan 0.0500 -0.0202
## 280 2.2169 nan 0.0500 -0.0109
## 300 2.1496 nan 0.0500 -0.0030
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.1857 nan 0.0500 4.8837
## 2 50.5688 nan 0.0500 4.1611
## 3 46.3689 nan 0.0500 4.7456
## 4 42.4907 nan 0.0500 3.8934
## 5 39.0269 nan 0.0500 3.1725
## 6 35.8638 nan 0.0500 2.9448
## 7 32.9522 nan 0.0500 2.9695
## 8 30.4349 nan 0.0500 2.5099
## 9 28.2084 nan 0.0500 2.2963
## 10 26.1367 nan 0.0500 2.2792
## 20 12.3870 nan 0.0500 0.7144
## 40 4.5159 nan 0.0500 0.1651
## 60 2.8716 nan 0.0500 0.0297
## 80 2.3045 nan 0.0500 -0.0115
## 100 1.9737 nan 0.0500 -0.0124
## 120 1.7930 nan 0.0500 -0.0118
## 140 1.6307 nan 0.0500 -0.0118
## 160 1.4851 nan 0.0500 -0.0045
## 180 1.3631 nan 0.0500 -0.0117
## 200 1.2643 nan 0.0500 -0.0136
## 220 1.1881 nan 0.0500 -0.0091
## 240 1.0961 nan 0.0500 -0.0063
## 260 1.0162 nan 0.0500 -0.0065
## 280 0.9550 nan 0.0500 -0.0126
## 300 0.8910 nan 0.0500 -0.0012
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.9232 nan 0.0500 5.3887
## 2 50.0496 nan 0.0500 4.9480
## 3 45.7527 nan 0.0500 4.1426
## 4 41.8920 nan 0.0500 4.1011
## 5 38.1925 nan 0.0500 3.4646
## 6 35.0637 nan 0.0500 3.4100
## 7 32.3189 nan 0.0500 2.8284
## 8 29.6359 nan 0.0500 2.8051
## 9 27.2683 nan 0.0500 2.7251
## 10 25.3115 nan 0.0500 2.0270
## 20 12.1808 nan 0.0500 0.7856
## 40 4.5920 nan 0.0500 0.0962
## 60 3.0367 nan 0.0500 0.0119
## 80 2.5324 nan 0.0500 -0.0127
## 100 2.2956 nan 0.0500 -0.0173
## 120 2.1016 nan 0.0500 -0.0085
## 140 1.9468 nan 0.0500 -0.0129
## 160 1.8193 nan 0.0500 -0.0090
## 180 1.6931 nan 0.0500 -0.0049
## 200 1.5853 nan 0.0500 -0.0100
## 220 1.4886 nan 0.0500 -0.0144
## 240 1.4078 nan 0.0500 -0.0171
## 260 1.3378 nan 0.0500 -0.0051
## 280 1.2736 nan 0.0500 -0.0133
## 300 1.2098 nan 0.0500 -0.0065
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.3440 nan 0.0500 5.2930
## 2 50.6264 nan 0.0500 4.0565
## 3 46.1377 nan 0.0500 3.7722
## 4 42.3810 nan 0.0500 3.9496
## 5 38.8966 nan 0.0500 3.2894
## 6 35.7395 nan 0.0500 3.0532
## 7 32.7523 nan 0.0500 2.7270
## 8 30.1362 nan 0.0500 2.4489
## 9 27.8266 nan 0.0500 2.5671
## 10 25.8540 nan 0.0500 2.0192
## 20 12.7242 nan 0.0500 0.7560
## 40 4.9571 nan 0.0500 0.0860
## 60 3.5689 nan 0.0500 0.0199
## 80 3.0792 nan 0.0500 -0.0008
## 100 2.7888 nan 0.0500 -0.0108
## 120 2.5822 nan 0.0500 -0.0168
## 140 2.4062 nan 0.0500 -0.0144
## 160 2.2650 nan 0.0500 -0.0160
## 180 2.1387 nan 0.0500 -0.0104
## 200 2.0378 nan 0.0500 -0.0226
## 220 1.9611 nan 0.0500 -0.0134
## 240 1.8733 nan 0.0500 -0.0130
## 260 1.7797 nan 0.0500 -0.0064
## 280 1.7152 nan 0.0500 -0.0095
## 300 1.6319 nan 0.0500 -0.0218
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.4413 nan 0.1000 7.0482
## 2 46.9345 nan 0.1000 6.0775
## 3 42.1806 nan 0.1000 4.9367
## 4 37.7369 nan 0.1000 3.8293
## 5 34.1193 nan 0.1000 3.6720
## 6 30.9113 nan 0.1000 3.1331
## 7 28.2375 nan 0.1000 2.3688
## 8 25.6355 nan 0.1000 2.0058
## 9 23.4848 nan 0.1000 1.9998
## 10 21.5960 nan 0.1000 1.8265
## 20 11.2122 nan 0.1000 0.5703
## 40 5.5294 nan 0.1000 0.0751
## 60 4.0125 nan 0.1000 0.0272
## 80 3.5826 nan 0.1000 -0.0205
## 100 3.3283 nan 0.1000 -0.0180
## 120 3.1888 nan 0.1000 -0.0183
## 140 3.0648 nan 0.1000 0.0043
## 160 2.9676 nan 0.1000 -0.0046
## 180 2.8878 nan 0.1000 -0.0253
## 200 2.8500 nan 0.1000 -0.0109
## 220 2.7893 nan 0.1000 -0.0068
## 240 2.7399 nan 0.1000 -0.0125
## 260 2.6782 nan 0.1000 -0.0181
## 280 2.6396 nan 0.1000 -0.0284
## 300 2.5996 nan 0.1000 -0.0092
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.2113 nan 0.1000 7.2185
## 2 47.1566 nan 0.1000 5.7679
## 3 42.4754 nan 0.1000 4.8436
## 4 38.4897 nan 0.1000 3.7088
## 5 34.5489 nan 0.1000 4.0258
## 6 31.0548 nan 0.1000 3.4631
## 7 28.3184 nan 0.1000 2.6479
## 8 25.9242 nan 0.1000 2.3159
## 9 23.7075 nan 0.1000 2.3142
## 10 21.6945 nan 0.1000 1.4562
## 20 11.0545 nan 0.1000 0.4796
## 40 5.3072 nan 0.1000 0.0443
## 60 4.0285 nan 0.1000 0.0102
## 80 3.6023 nan 0.1000 0.0107
## 100 3.3816 nan 0.1000 -0.0131
## 120 3.2236 nan 0.1000 -0.0061
## 140 3.1159 nan 0.1000 -0.0056
## 160 3.0313 nan 0.1000 -0.0047
## 180 2.9501 nan 0.1000 -0.0023
## 200 2.9048 nan 0.1000 -0.0199
## 220 2.8509 nan 0.1000 -0.0022
## 240 2.7995 nan 0.1000 0.0019
## 260 2.7515 nan 0.1000 -0.0059
## 280 2.7220 nan 0.1000 -0.0070
## 300 2.6959 nan 0.1000 -0.0108
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.8100 nan 0.1000 7.3750
## 2 46.6933 nan 0.1000 5.6288
## 3 42.1308 nan 0.1000 4.7390
## 4 37.9086 nan 0.1000 4.4668
## 5 33.9932 nan 0.1000 3.5485
## 6 30.6513 nan 0.1000 2.8972
## 7 27.9182 nan 0.1000 2.4862
## 8 25.3093 nan 0.1000 2.3595
## 9 23.3493 nan 0.1000 1.8550
## 10 21.4531 nan 0.1000 2.0171
## 20 11.1004 nan 0.1000 0.5512
## 40 5.7002 nan 0.1000 0.0896
## 60 4.5496 nan 0.1000 -0.0396
## 80 4.1940 nan 0.1000 -0.0272
## 100 3.9786 nan 0.1000 0.0008
## 120 3.8166 nan 0.1000 -0.0010
## 140 3.6624 nan 0.1000 -0.0052
## 160 3.5650 nan 0.1000 -0.0129
## 180 3.4765 nan 0.1000 -0.0209
## 200 3.3868 nan 0.1000 -0.0115
## 220 3.3098 nan 0.1000 -0.0210
## 240 3.2425 nan 0.1000 -0.0056
## 260 3.1999 nan 0.1000 -0.0250
## 280 3.1466 nan 0.1000 -0.0046
## 300 3.0790 nan 0.1000 -0.0271
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.1775 nan 0.1000 9.4080
## 2 43.8478 nan 0.1000 7.6566
## 3 37.2354 nan 0.1000 6.7593
## 4 32.0868 nan 0.1000 5.3496
## 5 27.7231 nan 0.1000 3.8389
## 6 23.8354 nan 0.1000 3.4698
## 7 20.7364 nan 0.1000 2.8912
## 8 18.2526 nan 0.1000 2.3685
## 9 16.0634 nan 0.1000 1.8626
## 10 14.3530 nan 0.1000 1.8486
## 20 5.7293 nan 0.1000 0.3496
## 40 3.0684 nan 0.1000 -0.0380
## 60 2.5188 nan 0.1000 -0.0051
## 80 2.1796 nan 0.1000 -0.0119
## 100 1.9204 nan 0.1000 -0.0369
## 120 1.6951 nan 0.1000 -0.0180
## 140 1.5568 nan 0.1000 -0.0047
## 160 1.4297 nan 0.1000 -0.0074
## 180 1.3127 nan 0.1000 -0.0126
## 200 1.1961 nan 0.1000 -0.0097
## 220 1.1163 nan 0.1000 -0.0077
## 240 1.0486 nan 0.1000 -0.0216
## 260 0.9690 nan 0.1000 -0.0203
## 280 0.9110 nan 0.1000 -0.0192
## 300 0.8538 nan 0.1000 -0.0098
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.6595 nan 0.1000 9.9095
## 2 42.9095 nan 0.1000 7.9389
## 3 36.5242 nan 0.1000 6.2546
## 4 31.4446 nan 0.1000 4.7792
## 5 26.5409 nan 0.1000 4.5327
## 6 23.0049 nan 0.1000 3.4396
## 7 20.1019 nan 0.1000 2.6274
## 8 17.6506 nan 0.1000 2.2507
## 9 15.5127 nan 0.1000 1.6599
## 10 13.9107 nan 0.1000 1.5237
## 20 5.8154 nan 0.1000 0.3539
## 40 3.1334 nan 0.1000 -0.0066
## 60 2.6766 nan 0.1000 -0.0162
## 80 2.4024 nan 0.1000 -0.0175
## 100 2.2158 nan 0.1000 -0.0592
## 120 2.0456 nan 0.1000 -0.0226
## 140 1.9040 nan 0.1000 -0.0156
## 160 1.7839 nan 0.1000 -0.0246
## 180 1.6838 nan 0.1000 -0.0288
## 200 1.5742 nan 0.1000 -0.0162
## 220 1.4782 nan 0.1000 -0.0133
## 240 1.4086 nan 0.1000 -0.0134
## 260 1.3391 nan 0.1000 -0.0210
## 280 1.2640 nan 0.1000 -0.0106
## 300 1.1993 nan 0.1000 -0.0079
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.4600 nan 0.1000 9.2256
## 2 42.8153 nan 0.1000 7.4669
## 3 36.6060 nan 0.1000 6.2964
## 4 31.3247 nan 0.1000 5.5919
## 5 27.0207 nan 0.1000 4.1177
## 6 23.5415 nan 0.1000 3.0755
## 7 20.6148 nan 0.1000 2.8223
## 8 18.1067 nan 0.1000 1.9492
## 9 15.8204 nan 0.1000 2.0795
## 10 14.2154 nan 0.1000 1.6097
## 20 6.1504 nan 0.1000 0.3035
## 40 3.6257 nan 0.1000 0.0059
## 60 3.1902 nan 0.1000 -0.0181
## 80 2.8918 nan 0.1000 -0.0603
## 100 2.6322 nan 0.1000 -0.0145
## 120 2.4311 nan 0.1000 -0.0105
## 140 2.2845 nan 0.1000 -0.0244
## 160 2.1437 nan 0.1000 -0.0340
## 180 2.0417 nan 0.1000 -0.0266
## 200 1.9398 nan 0.1000 -0.0200
## 220 1.8479 nan 0.1000 -0.0156
## 240 1.7231 nan 0.1000 -0.0198
## 260 1.6472 nan 0.1000 -0.0202
## 280 1.5822 nan 0.1000 -0.0149
## 300 1.5092 nan 0.1000 -0.0172
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.1278 nan 0.1000 10.7639
## 2 41.9169 nan 0.1000 7.7750
## 3 34.8019 nan 0.1000 6.7118
## 4 29.0119 nan 0.1000 5.8775
## 5 24.5851 nan 0.1000 3.9730
## 6 21.0382 nan 0.1000 3.7615
## 7 18.2798 nan 0.1000 2.5555
## 8 15.6987 nan 0.1000 2.2117
## 9 13.7514 nan 0.1000 1.8226
## 10 11.9778 nan 0.1000 1.7120
## 20 4.3139 nan 0.1000 0.2336
## 40 2.4684 nan 0.1000 -0.0115
## 60 1.9714 nan 0.1000 -0.0249
## 80 1.6010 nan 0.1000 -0.0175
## 100 1.3475 nan 0.1000 -0.0084
## 120 1.1572 nan 0.1000 -0.0148
## 140 0.9967 nan 0.1000 -0.0252
## 160 0.8725 nan 0.1000 -0.0107
## 180 0.7739 nan 0.1000 -0.0212
## 200 0.6847 nan 0.1000 -0.0145
## 220 0.6143 nan 0.1000 -0.0090
## 240 0.5348 nan 0.1000 -0.0105
## 260 0.4857 nan 0.1000 -0.0141
## 280 0.4352 nan 0.1000 -0.0110
## 300 0.3886 nan 0.1000 -0.0026
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.6709 nan 0.1000 9.8987
## 2 42.1694 nan 0.1000 7.3982
## 3 35.4002 nan 0.1000 6.2556
## 4 29.6558 nan 0.1000 5.4455
## 5 25.0474 nan 0.1000 4.9400
## 6 21.3173 nan 0.1000 3.3580
## 7 18.3288 nan 0.1000 2.7970
## 8 15.6790 nan 0.1000 2.3312
## 9 13.6829 nan 0.1000 2.0718
## 10 11.9804 nan 0.1000 1.7679
## 20 4.5127 nan 0.1000 0.3171
## 40 2.7647 nan 0.1000 -0.0138
## 60 2.2612 nan 0.1000 -0.0361
## 80 1.9090 nan 0.1000 -0.0370
## 100 1.6473 nan 0.1000 -0.0123
## 120 1.4679 nan 0.1000 -0.0437
## 140 1.3311 nan 0.1000 -0.0131
## 160 1.2084 nan 0.1000 -0.0261
## 180 1.1072 nan 0.1000 -0.0161
## 200 0.9940 nan 0.1000 -0.0125
## 220 0.9182 nan 0.1000 -0.0248
## 240 0.8526 nan 0.1000 -0.0100
## 260 0.7790 nan 0.1000 -0.0075
## 280 0.7199 nan 0.1000 -0.0123
## 300 0.6626 nan 0.1000 -0.0111
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.4166 nan 0.1000 9.7871
## 2 42.3009 nan 0.1000 7.8237
## 3 35.7272 nan 0.1000 7.6116
## 4 30.3339 nan 0.1000 5.4545
## 5 25.7600 nan 0.1000 4.6163
## 6 21.9639 nan 0.1000 3.6043
## 7 18.8351 nan 0.1000 2.8494
## 8 16.3420 nan 0.1000 2.4566
## 9 14.3548 nan 0.1000 1.8962
## 10 12.6935 nan 0.1000 1.5300
## 20 5.0710 nan 0.1000 0.2029
## 40 3.1581 nan 0.1000 -0.0205
## 60 2.6147 nan 0.1000 -0.0018
## 80 2.3475 nan 0.1000 -0.0460
## 100 2.1062 nan 0.1000 -0.0165
## 120 1.8971 nan 0.1000 -0.0157
## 140 1.7357 nan 0.1000 -0.0366
## 160 1.6097 nan 0.1000 -0.0175
## 180 1.4851 nan 0.1000 -0.0193
## 200 1.3963 nan 0.1000 -0.0271
## 220 1.3023 nan 0.1000 -0.0240
## 240 1.2026 nan 0.1000 -0.0187
## 260 1.1243 nan 0.1000 -0.0156
## 280 1.0559 nan 0.1000 -0.0148
## 300 0.9832 nan 0.1000 -0.0083
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.2250 nan 0.0100 0.8080
## 2 61.3759 nan 0.0100 0.7540
## 3 60.5950 nan 0.0100 0.7500
## 4 59.8375 nan 0.0100 0.7442
## 5 59.1443 nan 0.0100 0.7453
## 6 58.4077 nan 0.0100 0.7405
## 7 57.6459 nan 0.0100 0.7713
## 8 56.9483 nan 0.0100 0.6874
## 9 56.2531 nan 0.0100 0.7130
## 10 55.5600 nan 0.0100 0.6723
## 20 49.1877 nan 0.0100 0.5552
## 40 39.4816 nan 0.0100 0.3879
## 60 32.2214 nan 0.0100 0.3467
## 80 26.6893 nan 0.0100 0.2106
## 100 22.5130 nan 0.0100 0.1659
## 120 19.3795 nan 0.0100 0.1082
## 140 16.7304 nan 0.0100 0.0808
## 160 14.6506 nan 0.0100 0.0708
## 180 12.8703 nan 0.0100 0.0727
## 200 11.4987 nan 0.0100 0.0539
## 220 10.3751 nan 0.0100 0.0643
## 240 9.4425 nan 0.0100 0.0187
## 260 8.6003 nan 0.0100 0.0313
## 280 7.9025 nan 0.0100 0.0245
## 300 7.3315 nan 0.0100 0.0221
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.1775 nan 0.0100 0.7423
## 2 61.3921 nan 0.0100 0.7972
## 3 60.5523 nan 0.0100 0.7621
## 4 59.7922 nan 0.0100 0.7217
## 5 59.0384 nan 0.0100 0.7266
## 6 58.2876 nan 0.0100 0.6224
## 7 57.5883 nan 0.0100 0.7396
## 8 56.8261 nan 0.0100 0.6935
## 9 56.1379 nan 0.0100 0.7154
## 10 55.3895 nan 0.0100 0.6789
## 20 49.0788 nan 0.0100 0.5400
## 40 39.3837 nan 0.0100 0.3810
## 60 32.0102 nan 0.0100 0.2772
## 80 26.4681 nan 0.0100 0.1781
## 100 22.1948 nan 0.0100 0.1660
## 120 18.9428 nan 0.0100 0.1269
## 140 16.3706 nan 0.0100 0.1015
## 160 14.3054 nan 0.0100 0.0745
## 180 12.6997 nan 0.0100 0.0508
## 200 11.3268 nan 0.0100 0.0539
## 220 10.2240 nan 0.0100 0.0358
## 240 9.3001 nan 0.0100 0.0398
## 260 8.5537 nan 0.0100 0.0327
## 280 7.8663 nan 0.0100 0.0213
## 300 7.2647 nan 0.0100 0.0184
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.1290 nan 0.0100 0.8067
## 2 61.3155 nan 0.0100 0.7800
## 3 60.5204 nan 0.0100 0.7802
## 4 59.7427 nan 0.0100 0.7059
## 5 58.9736 nan 0.0100 0.7207
## 6 58.2447 nan 0.0100 0.7615
## 7 57.5576 nan 0.0100 0.7383
## 8 56.8487 nan 0.0100 0.7156
## 9 56.1289 nan 0.0100 0.6327
## 10 55.4179 nan 0.0100 0.6504
## 20 48.9773 nan 0.0100 0.5425
## 40 39.3361 nan 0.0100 0.4192
## 60 32.2009 nan 0.0100 0.2949
## 80 26.6833 nan 0.0100 0.2309
## 100 22.4815 nan 0.0100 0.1388
## 120 19.2056 nan 0.0100 0.1191
## 140 16.6507 nan 0.0100 0.0993
## 160 14.5210 nan 0.0100 0.0797
## 180 12.8835 nan 0.0100 0.0626
## 200 11.4841 nan 0.0100 0.0604
## 220 10.3122 nan 0.0100 0.0395
## 240 9.3963 nan 0.0100 0.0345
## 260 8.5962 nan 0.0100 0.0276
## 280 7.9332 nan 0.0100 0.0344
## 300 7.3494 nan 0.0100 0.0164
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.9338 nan 0.0100 1.0558
## 2 60.9236 nan 0.0100 0.9808
## 3 59.9626 nan 0.0100 0.9892
## 4 58.9619 nan 0.0100 1.0130
## 5 58.0657 nan 0.0100 0.8618
## 6 57.0910 nan 0.0100 0.8310
## 7 56.1803 nan 0.0100 0.9299
## 8 55.2932 nan 0.0100 0.8940
## 9 54.3656 nan 0.0100 0.8518
## 10 53.4433 nan 0.0100 0.8041
## 20 45.3767 nan 0.0100 0.6943
## 40 32.9992 nan 0.0100 0.4429
## 60 24.6285 nan 0.0100 0.3374
## 80 18.7587 nan 0.0100 0.2427
## 100 14.6550 nan 0.0100 0.1579
## 120 11.6727 nan 0.0100 0.1054
## 140 9.5370 nan 0.0100 0.0898
## 160 7.9515 nan 0.0100 0.0635
## 180 6.7901 nan 0.0100 0.0389
## 200 5.8707 nan 0.0100 0.0169
## 220 5.2179 nan 0.0100 0.0228
## 240 4.6995 nan 0.0100 0.0198
## 260 4.3069 nan 0.0100 0.0119
## 280 3.9777 nan 0.0100 0.0129
## 300 3.7251 nan 0.0100 0.0042
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.9252 nan 0.0100 1.0553
## 2 60.9329 nan 0.0100 1.0044
## 3 59.8752 nan 0.0100 0.8082
## 4 58.8918 nan 0.0100 1.0154
## 5 57.9231 nan 0.0100 0.9801
## 6 56.9689 nan 0.0100 0.9592
## 7 56.0481 nan 0.0100 0.9219
## 8 55.1753 nan 0.0100 0.9613
## 9 54.3024 nan 0.0100 0.8195
## 10 53.4295 nan 0.0100 0.9299
## 20 45.4755 nan 0.0100 0.6437
## 40 33.1642 nan 0.0100 0.4892
## 60 24.6195 nan 0.0100 0.3006
## 80 18.7592 nan 0.0100 0.2456
## 100 14.6244 nan 0.0100 0.1633
## 120 11.7325 nan 0.0100 0.1171
## 140 9.6111 nan 0.0100 0.0894
## 160 8.0001 nan 0.0100 0.0465
## 180 6.8428 nan 0.0100 0.0429
## 200 5.9700 nan 0.0100 0.0296
## 220 5.2908 nan 0.0100 0.0274
## 240 4.7608 nan 0.0100 0.0190
## 260 4.3717 nan 0.0100 0.0144
## 280 4.0627 nan 0.0100 0.0026
## 300 3.8414 nan 0.0100 0.0017
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.9034 nan 0.0100 1.0957
## 2 60.8535 nan 0.0100 1.1387
## 3 59.8447 nan 0.0100 1.0560
## 4 58.8592 nan 0.0100 1.0239
## 5 57.8631 nan 0.0100 0.8508
## 6 56.8872 nan 0.0100 0.8977
## 7 55.9178 nan 0.0100 0.9576
## 8 55.0172 nan 0.0100 0.8836
## 9 54.1250 nan 0.0100 0.9736
## 10 53.2791 nan 0.0100 0.8746
## 20 45.2750 nan 0.0100 0.6954
## 40 33.1562 nan 0.0100 0.4326
## 60 24.7718 nan 0.0100 0.3034
## 80 18.8867 nan 0.0100 0.2200
## 100 14.7351 nan 0.0100 0.1555
## 120 11.8158 nan 0.0100 0.1176
## 140 9.6989 nan 0.0100 0.0874
## 160 8.1425 nan 0.0100 0.0566
## 180 6.9508 nan 0.0100 0.0434
## 200 6.1180 nan 0.0100 0.0382
## 220 5.4361 nan 0.0100 0.0208
## 240 4.9255 nan 0.0100 0.0163
## 260 4.5287 nan 0.0100 0.0129
## 280 4.2315 nan 0.0100 0.0074
## 300 4.0083 nan 0.0100 0.0056
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.8840 nan 0.0100 1.1042
## 2 60.7707 nan 0.0100 1.0476
## 3 59.7050 nan 0.0100 0.9901
## 4 58.6289 nan 0.0100 0.9761
## 5 57.5941 nan 0.0100 1.0617
## 6 56.6050 nan 0.0100 0.8487
## 7 55.5825 nan 0.0100 0.7780
## 8 54.6082 nan 0.0100 0.9778
## 9 53.6413 nan 0.0100 0.8950
## 10 52.7058 nan 0.0100 0.8485
## 20 44.3335 nan 0.0100 0.7864
## 40 31.7128 nan 0.0100 0.5009
## 60 23.0027 nan 0.0100 0.3533
## 80 17.1075 nan 0.0100 0.2456
## 100 12.9496 nan 0.0100 0.1677
## 120 10.0180 nan 0.0100 0.1198
## 140 7.9927 nan 0.0100 0.0788
## 160 6.5216 nan 0.0100 0.0489
## 180 5.4869 nan 0.0100 0.0387
## 200 4.7378 nan 0.0100 0.0282
## 220 4.1741 nan 0.0100 0.0174
## 240 3.7455 nan 0.0100 0.0069
## 260 3.4255 nan 0.0100 0.0118
## 280 3.1851 nan 0.0100 0.0039
## 300 2.9918 nan 0.0100 -0.0033
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.9029 nan 0.0100 1.0838
## 2 60.8524 nan 0.0100 1.0358
## 3 59.8081 nan 0.0100 1.0544
## 4 58.7475 nan 0.0100 0.9792
## 5 57.6785 nan 0.0100 1.1190
## 6 56.6316 nan 0.0100 0.9687
## 7 55.6589 nan 0.0100 0.9899
## 8 54.7260 nan 0.0100 0.9178
## 9 53.7906 nan 0.0100 0.9544
## 10 52.8549 nan 0.0100 0.9613
## 20 44.4998 nan 0.0100 0.7896
## 40 31.8305 nan 0.0100 0.5433
## 60 23.1911 nan 0.0100 0.3793
## 80 17.1612 nan 0.0100 0.2516
## 100 13.0597 nan 0.0100 0.1660
## 120 10.1861 nan 0.0100 0.1142
## 140 8.0607 nan 0.0100 0.0813
## 160 6.6720 nan 0.0100 0.0446
## 180 5.6181 nan 0.0100 0.0284
## 200 4.8530 nan 0.0100 0.0289
## 220 4.3298 nan 0.0100 0.0119
## 240 3.9379 nan 0.0100 0.0124
## 260 3.6107 nan 0.0100 0.0093
## 280 3.3905 nan 0.0100 0.0069
## 300 3.2099 nan 0.0100 0.0013
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.8262 nan 0.0100 1.0282
## 2 60.7414 nan 0.0100 0.9300
## 3 59.7287 nan 0.0100 1.0469
## 4 58.7038 nan 0.0100 0.9661
## 5 57.6772 nan 0.0100 1.0138
## 6 56.7184 nan 0.0100 1.0387
## 7 55.7318 nan 0.0100 0.9497
## 8 54.7784 nan 0.0100 0.8475
## 9 53.8206 nan 0.0100 0.9277
## 10 52.8755 nan 0.0100 0.9244
## 20 44.5045 nan 0.0100 0.7444
## 40 31.9332 nan 0.0100 0.4698
## 60 23.2921 nan 0.0100 0.3239
## 80 17.3321 nan 0.0100 0.2311
## 100 13.2045 nan 0.0100 0.1453
## 120 10.3639 nan 0.0100 0.1160
## 140 8.3058 nan 0.0100 0.0827
## 160 6.8461 nan 0.0100 0.0571
## 180 5.8602 nan 0.0100 0.0313
## 200 5.0924 nan 0.0100 0.0289
## 220 4.5788 nan 0.0100 0.0199
## 240 4.1879 nan 0.0100 0.0120
## 260 3.9032 nan 0.0100 0.0097
## 280 3.6794 nan 0.0100 0.0018
## 300 3.5187 nan 0.0100 0.0035
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.1992 nan 0.0500 3.7633
## 2 55.8039 nan 0.0500 3.8103
## 3 52.4253 nan 0.0500 3.2032
## 4 49.5985 nan 0.0500 2.9521
## 5 46.9727 nan 0.0500 2.7187
## 6 44.4842 nan 0.0500 2.6494
## 7 42.5803 nan 0.0500 1.9475
## 8 40.0947 nan 0.0500 1.8446
## 9 38.0131 nan 0.0500 1.7675
## 10 36.3670 nan 0.0500 1.8697
## 20 22.7345 nan 0.0500 0.9666
## 40 11.5011 nan 0.0500 0.2560
## 60 7.3001 nan 0.0500 0.1227
## 80 5.4083 nan 0.0500 0.0197
## 100 4.4284 nan 0.0500 0.0123
## 120 3.9571 nan 0.0500 0.0030
## 140 3.7201 nan 0.0500 0.0027
## 160 3.5906 nan 0.0500 -0.0192
## 180 3.4824 nan 0.0500 -0.0068
## 200 3.4019 nan 0.0500 -0.0050
## 220 3.3336 nan 0.0500 -0.0081
## 240 3.2663 nan 0.0500 -0.0091
## 260 3.2168 nan 0.0500 -0.0033
## 280 3.1686 nan 0.0500 -0.0040
## 300 3.1305 nan 0.0500 -0.0039
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.8162 nan 0.0500 4.2199
## 2 54.8643 nan 0.0500 3.4326
## 3 51.5848 nan 0.0500 3.5052
## 4 48.7158 nan 0.0500 2.6763
## 5 46.0633 nan 0.0500 2.8754
## 6 43.5795 nan 0.0500 2.2300
## 7 41.1610 nan 0.0500 2.1220
## 8 38.8422 nan 0.0500 2.0800
## 9 36.7573 nan 0.0500 1.8519
## 10 34.9317 nan 0.0500 1.8425
## 20 21.6887 nan 0.0500 0.7019
## 40 11.0907 nan 0.0500 0.2682
## 60 7.1343 nan 0.0500 0.0684
## 80 5.3020 nan 0.0500 0.0193
## 100 4.4152 nan 0.0500 0.0124
## 120 4.0134 nan 0.0500 0.0157
## 140 3.7967 nan 0.0500 0.0009
## 160 3.6704 nan 0.0500 0.0033
## 180 3.5799 nan 0.0500 0.0016
## 200 3.5053 nan 0.0500 -0.0118
## 220 3.4409 nan 0.0500 -0.0051
## 240 3.3934 nan 0.0500 -0.0114
## 260 3.3438 nan 0.0500 -0.0073
## 280 3.2999 nan 0.0500 -0.0018
## 300 3.2459 nan 0.0500 -0.0071
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.4357 nan 0.0500 3.5867
## 2 55.5791 nan 0.0500 3.6253
## 3 52.4047 nan 0.0500 3.2569
## 4 49.1121 nan 0.0500 2.8698
## 5 46.2663 nan 0.0500 2.7743
## 6 43.5191 nan 0.0500 2.8597
## 7 41.1253 nan 0.0500 1.9907
## 8 38.7613 nan 0.0500 2.2448
## 9 36.6487 nan 0.0500 1.7755
## 10 34.5699 nan 0.0500 1.7496
## 20 22.0686 nan 0.0500 0.8324
## 40 11.3078 nan 0.0500 0.2082
## 60 7.2191 nan 0.0500 0.0738
## 80 5.3958 nan 0.0500 0.0586
## 100 4.5693 nan 0.0500 0.0168
## 120 4.1460 nan 0.0500 0.0076
## 140 3.9373 nan 0.0500 0.0060
## 160 3.8132 nan 0.0500 -0.0176
## 180 3.7193 nan 0.0500 0.0002
## 200 3.6579 nan 0.0500 -0.0018
## 220 3.5893 nan 0.0500 -0.0002
## 240 3.5393 nan 0.0500 -0.0145
## 260 3.4872 nan 0.0500 -0.0031
## 280 3.4536 nan 0.0500 -0.0060
## 300 3.4053 nan 0.0500 -0.0050
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.7751 nan 0.0500 5.4923
## 2 52.9072 nan 0.0500 4.4656
## 3 48.8432 nan 0.0500 4.1989
## 4 44.9278 nan 0.0500 4.0555
## 5 41.5002 nan 0.0500 3.5311
## 6 38.2291 nan 0.0500 3.2110
## 7 35.3868 nan 0.0500 2.9413
## 8 32.6454 nan 0.0500 2.7635
## 9 30.3262 nan 0.0500 2.3333
## 10 28.0794 nan 0.0500 2.0681
## 20 14.2866 nan 0.0500 0.7889
## 40 5.7829 nan 0.0500 0.1991
## 60 3.6033 nan 0.0500 0.0332
## 80 2.9941 nan 0.0500 0.0098
## 100 2.6870 nan 0.0500 -0.0096
## 120 2.5260 nan 0.0500 -0.0056
## 140 2.3545 nan 0.0500 0.0009
## 160 2.2110 nan 0.0500 -0.0150
## 180 2.0876 nan 0.0500 -0.0171
## 200 1.9880 nan 0.0500 -0.0159
## 220 1.8922 nan 0.0500 -0.0096
## 240 1.8132 nan 0.0500 -0.0145
## 260 1.7284 nan 0.0500 -0.0101
## 280 1.6496 nan 0.0500 -0.0081
## 300 1.5754 nan 0.0500 -0.0166
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.7875 nan 0.0500 5.2558
## 2 53.1611 nan 0.0500 4.5811
## 3 48.8136 nan 0.0500 4.1785
## 4 44.9329 nan 0.0500 3.3738
## 5 41.6542 nan 0.0500 3.0806
## 6 38.4826 nan 0.0500 2.9178
## 7 35.4393 nan 0.0500 3.0682
## 8 32.7793 nan 0.0500 2.6241
## 9 30.3208 nan 0.0500 2.4860
## 10 28.1855 nan 0.0500 1.9496
## 20 14.4514 nan 0.0500 0.8085
## 40 5.9206 nan 0.0500 0.1137
## 60 3.9843 nan 0.0500 0.0292
## 80 3.3317 nan 0.0500 -0.0128
## 100 3.0584 nan 0.0500 -0.0193
## 120 2.8309 nan 0.0500 -0.0124
## 140 2.6867 nan 0.0500 -0.0184
## 160 2.5395 nan 0.0500 -0.0179
## 180 2.4297 nan 0.0500 -0.0101
## 200 2.3229 nan 0.0500 -0.0101
## 220 2.2192 nan 0.0500 -0.0077
## 240 2.1290 nan 0.0500 -0.0175
## 260 2.0491 nan 0.0500 -0.0108
## 280 1.9831 nan 0.0500 -0.0050
## 300 1.9239 nan 0.0500 -0.0077
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.8154 nan 0.0500 4.7682
## 2 53.2368 nan 0.0500 4.7700
## 3 49.1382 nan 0.0500 4.2446
## 4 45.3072 nan 0.0500 3.7644
## 5 41.8973 nan 0.0500 3.4410
## 6 38.6171 nan 0.0500 2.9502
## 7 35.6895 nan 0.0500 2.8480
## 8 33.1026 nan 0.0500 2.4932
## 9 30.5648 nan 0.0500 2.1850
## 10 28.5676 nan 0.0500 2.1028
## 20 14.6785 nan 0.0500 0.8945
## 40 5.9461 nan 0.0500 0.1669
## 60 4.0232 nan 0.0500 0.0260
## 80 3.4272 nan 0.0500 -0.0083
## 100 3.1486 nan 0.0500 -0.0021
## 120 2.9701 nan 0.0500 -0.0131
## 140 2.8023 nan 0.0500 -0.0153
## 160 2.6882 nan 0.0500 -0.0219
## 180 2.5674 nan 0.0500 -0.0103
## 200 2.4610 nan 0.0500 -0.0146
## 220 2.3808 nan 0.0500 -0.0063
## 240 2.3006 nan 0.0500 -0.0073
## 260 2.2280 nan 0.0500 -0.0071
## 280 2.1721 nan 0.0500 -0.0162
## 300 2.1116 nan 0.0500 -0.0159
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.5632 nan 0.0500 5.6227
## 2 52.3703 nan 0.0500 5.1648
## 3 47.7481 nan 0.0500 4.3406
## 4 43.6565 nan 0.0500 4.5368
## 5 40.0569 nan 0.0500 2.9786
## 6 36.6729 nan 0.0500 3.5080
## 7 33.5570 nan 0.0500 2.9001
## 8 30.7604 nan 0.0500 2.6136
## 9 28.1671 nan 0.0500 2.3857
## 10 26.0534 nan 0.0500 1.7687
## 20 12.2958 nan 0.0500 0.7652
## 40 4.5136 nan 0.0500 0.1056
## 60 2.9138 nan 0.0500 0.0070
## 80 2.3907 nan 0.0500 -0.0314
## 100 2.0729 nan 0.0500 -0.0151
## 120 1.8810 nan 0.0500 -0.0203
## 140 1.7020 nan 0.0500 -0.0083
## 160 1.5390 nan 0.0500 -0.0087
## 180 1.4004 nan 0.0500 -0.0052
## 200 1.2840 nan 0.0500 -0.0032
## 220 1.1975 nan 0.0500 -0.0032
## 240 1.0984 nan 0.0500 -0.0135
## 260 1.0196 nan 0.0500 -0.0085
## 280 0.9531 nan 0.0500 -0.0084
## 300 0.8882 nan 0.0500 -0.0063
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.5982 nan 0.0500 5.1419
## 2 52.7043 nan 0.0500 5.2285
## 3 48.2017 nan 0.0500 4.5196
## 4 44.0392 nan 0.0500 4.2655
## 5 40.4676 nan 0.0500 3.4111
## 6 37.1336 nan 0.0500 3.1229
## 7 34.1011 nan 0.0500 3.1204
## 8 31.2700 nan 0.0500 2.4294
## 9 28.8297 nan 0.0500 2.3109
## 10 26.6413 nan 0.0500 1.9269
## 20 12.5885 nan 0.0500 0.8037
## 40 4.6634 nan 0.0500 0.1170
## 60 3.2042 nan 0.0500 0.0020
## 80 2.7327 nan 0.0500 -0.0121
## 100 2.4669 nan 0.0500 -0.0063
## 120 2.2736 nan 0.0500 -0.0336
## 140 2.1018 nan 0.0500 -0.0174
## 160 1.9830 nan 0.0500 -0.0081
## 180 1.8582 nan 0.0500 -0.0162
## 200 1.7204 nan 0.0500 -0.0102
## 220 1.6135 nan 0.0500 -0.0182
## 240 1.5285 nan 0.0500 -0.0076
## 260 1.4476 nan 0.0500 -0.0189
## 280 1.3701 nan 0.0500 -0.0123
## 300 1.3030 nan 0.0500 -0.0074
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.8167 nan 0.0500 5.4355
## 2 52.9151 nan 0.0500 4.9638
## 3 48.5077 nan 0.0500 4.4417
## 4 44.4762 nan 0.0500 4.2603
## 5 40.9281 nan 0.0500 3.1023
## 6 37.9542 nan 0.0500 3.2136
## 7 34.8536 nan 0.0500 3.1168
## 8 32.0196 nan 0.0500 2.7030
## 9 29.3508 nan 0.0500 2.3860
## 10 27.1544 nan 0.0500 2.1126
## 20 13.3726 nan 0.0500 0.8988
## 40 5.1666 nan 0.0500 0.1075
## 60 3.5493 nan 0.0500 -0.0049
## 80 3.0793 nan 0.0500 -0.0079
## 100 2.8443 nan 0.0500 -0.0295
## 120 2.6534 nan 0.0500 -0.0263
## 140 2.4687 nan 0.0500 -0.0089
## 160 2.3371 nan 0.0500 -0.0117
## 180 2.2094 nan 0.0500 -0.0177
## 200 2.0858 nan 0.0500 -0.0164
## 220 1.9686 nan 0.0500 -0.0107
## 240 1.8652 nan 0.0500 -0.0170
## 260 1.7810 nan 0.0500 -0.0092
## 280 1.7019 nan 0.0500 -0.0055
## 300 1.6366 nan 0.0500 -0.0249
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.8709 nan 0.1000 7.4844
## 2 48.8909 nan 0.1000 7.1654
## 3 43.3184 nan 0.1000 5.2283
## 4 38.6295 nan 0.1000 4.1805
## 5 34.9287 nan 0.1000 3.4725
## 6 31.7173 nan 0.1000 2.5913
## 7 29.3212 nan 0.1000 2.0871
## 8 26.7942 nan 0.1000 2.3069
## 9 24.3090 nan 0.1000 2.2137
## 10 22.3158 nan 0.1000 1.4802
## 20 11.1699 nan 0.1000 0.7516
## 40 5.3493 nan 0.1000 0.0571
## 60 4.1689 nan 0.1000 0.0173
## 80 3.7641 nan 0.1000 -0.0021
## 100 3.5711 nan 0.1000 -0.0369
## 120 3.4760 nan 0.1000 -0.0062
## 140 3.3479 nan 0.1000 -0.0188
## 160 3.2690 nan 0.1000 -0.0184
## 180 3.1594 nan 0.1000 -0.0287
## 200 3.1050 nan 0.1000 -0.0311
## 220 3.0130 nan 0.1000 -0.0134
## 240 2.9732 nan 0.1000 -0.0111
## 260 2.9131 nan 0.1000 -0.0225
## 280 2.8605 nan 0.1000 -0.0082
## 300 2.8109 nan 0.1000 -0.0155
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.0976 nan 0.1000 7.6380
## 2 48.9094 nan 0.1000 6.1164
## 3 43.9099 nan 0.1000 5.2759
## 4 38.8845 nan 0.1000 5.1913
## 5 35.0466 nan 0.1000 3.4813
## 6 31.1977 nan 0.1000 3.8546
## 7 28.5896 nan 0.1000 1.9545
## 8 25.9762 nan 0.1000 2.2206
## 9 23.6819 nan 0.1000 2.2322
## 10 21.8832 nan 0.1000 1.7804
## 20 11.1010 nan 0.1000 0.5533
## 40 5.4764 nan 0.1000 0.0533
## 60 4.1226 nan 0.1000 0.0117
## 80 3.8003 nan 0.1000 0.0030
## 100 3.6072 nan 0.1000 -0.0292
## 120 3.4601 nan 0.1000 -0.0065
## 140 3.3756 nan 0.1000 -0.0112
## 160 3.3047 nan 0.1000 -0.0079
## 180 3.2359 nan 0.1000 -0.0255
## 200 3.1807 nan 0.1000 -0.0178
## 220 3.1151 nan 0.1000 -0.0087
## 240 3.0685 nan 0.1000 -0.0178
## 260 3.0173 nan 0.1000 -0.0198
## 280 2.9703 nan 0.1000 -0.0207
## 300 2.9332 nan 0.1000 -0.0272
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.0434 nan 0.1000 6.9573
## 2 48.9979 nan 0.1000 6.3344
## 3 43.4028 nan 0.1000 5.1258
## 4 39.5284 nan 0.1000 3.9916
## 5 35.5220 nan 0.1000 3.7960
## 6 32.4224 nan 0.1000 2.6873
## 7 29.2578 nan 0.1000 3.7091
## 8 26.1247 nan 0.1000 2.6075
## 9 23.9631 nan 0.1000 1.9019
## 10 22.1365 nan 0.1000 1.9129
## 20 11.0655 nan 0.1000 0.4904
## 40 5.5943 nan 0.1000 0.1175
## 60 4.4320 nan 0.1000 -0.0231
## 80 4.1626 nan 0.1000 0.0019
## 100 3.9434 nan 0.1000 -0.0215
## 120 3.7856 nan 0.1000 -0.0157
## 140 3.6685 nan 0.1000 -0.0130
## 160 3.5767 nan 0.1000 -0.0338
## 180 3.4809 nan 0.1000 -0.0247
## 200 3.4153 nan 0.1000 -0.0260
## 220 3.3637 nan 0.1000 -0.0184
## 240 3.2997 nan 0.1000 -0.0220
## 260 3.2196 nan 0.1000 -0.0037
## 280 3.1752 nan 0.1000 -0.0276
## 300 3.1396 nan 0.1000 -0.0097
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.1825 nan 0.1000 8.8474
## 2 45.3544 nan 0.1000 8.0821
## 3 38.3531 nan 0.1000 6.8722
## 4 32.8831 nan 0.1000 5.5796
## 5 28.0763 nan 0.1000 4.7544
## 6 23.9805 nan 0.1000 4.0557
## 7 20.7236 nan 0.1000 2.8624
## 8 18.0459 nan 0.1000 2.3807
## 9 15.7382 nan 0.1000 2.2804
## 10 13.6443 nan 0.1000 1.9852
## 20 5.6796 nan 0.1000 0.2747
## 40 3.1328 nan 0.1000 -0.0065
## 60 2.6449 nan 0.1000 -0.0377
## 80 2.2908 nan 0.1000 -0.0236
## 100 2.0302 nan 0.1000 -0.0200
## 120 1.8393 nan 0.1000 -0.0223
## 140 1.6754 nan 0.1000 -0.0144
## 160 1.5510 nan 0.1000 -0.0144
## 180 1.4331 nan 0.1000 -0.0157
## 200 1.3177 nan 0.1000 -0.0295
## 220 1.2008 nan 0.1000 -0.0218
## 240 1.1064 nan 0.1000 -0.0262
## 260 1.0342 nan 0.1000 -0.0147
## 280 0.9668 nan 0.1000 -0.0105
## 300 0.9057 nan 0.1000 -0.0095
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.5868 nan 0.1000 9.8999
## 2 45.1692 nan 0.1000 8.3824
## 3 38.1811 nan 0.1000 7.8841
## 4 32.2389 nan 0.1000 6.2928
## 5 27.7172 nan 0.1000 4.2075
## 6 23.9832 nan 0.1000 3.9251
## 7 20.5821 nan 0.1000 2.7446
## 8 18.3428 nan 0.1000 2.7741
## 9 15.9871 nan 0.1000 2.0383
## 10 14.1993 nan 0.1000 1.6286
## 20 5.8069 nan 0.1000 0.3173
## 40 3.3438 nan 0.1000 -0.0011
## 60 2.8205 nan 0.1000 -0.0395
## 80 2.5355 nan 0.1000 -0.0163
## 100 2.3143 nan 0.1000 0.0067
## 120 2.1039 nan 0.1000 -0.0054
## 140 1.9622 nan 0.1000 -0.0239
## 160 1.8067 nan 0.1000 -0.0193
## 180 1.7008 nan 0.1000 -0.0135
## 200 1.5884 nan 0.1000 -0.0142
## 220 1.5108 nan 0.1000 -0.0246
## 240 1.4212 nan 0.1000 -0.0177
## 260 1.3480 nan 0.1000 -0.0240
## 280 1.2914 nan 0.1000 -0.0068
## 300 1.2170 nan 0.1000 -0.0182
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.0748 nan 0.1000 10.6753
## 2 44.7872 nan 0.1000 8.1787
## 3 38.2032 nan 0.1000 6.4469
## 4 32.6412 nan 0.1000 5.0470
## 5 27.9569 nan 0.1000 4.3828
## 6 24.0892 nan 0.1000 4.1616
## 7 20.7450 nan 0.1000 3.2831
## 8 18.3223 nan 0.1000 2.6124
## 9 16.1706 nan 0.1000 2.0841
## 10 14.3961 nan 0.1000 1.6339
## 20 6.1383 nan 0.1000 0.1825
## 40 3.5999 nan 0.1000 -0.0380
## 60 3.1642 nan 0.1000 -0.0276
## 80 2.8800 nan 0.1000 -0.0243
## 100 2.6763 nan 0.1000 -0.0254
## 120 2.4645 nan 0.1000 -0.0148
## 140 2.3007 nan 0.1000 -0.0425
## 160 2.1856 nan 0.1000 -0.0465
## 180 2.0540 nan 0.1000 -0.0054
## 200 1.9323 nan 0.1000 -0.0259
## 220 1.8353 nan 0.1000 -0.0195
## 240 1.7186 nan 0.1000 -0.0106
## 260 1.6306 nan 0.1000 -0.0263
## 280 1.5604 nan 0.1000 -0.0124
## 300 1.4810 nan 0.1000 -0.0175
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.5608 nan 0.1000 10.3111
## 2 43.9035 nan 0.1000 9.1471
## 3 36.7392 nan 0.1000 7.2499
## 4 31.0089 nan 0.1000 5.8657
## 5 26.3105 nan 0.1000 4.5241
## 6 22.4058 nan 0.1000 3.9436
## 7 19.1915 nan 0.1000 3.0491
## 8 16.4741 nan 0.1000 2.5795
## 9 14.2474 nan 0.1000 2.2784
## 10 12.5049 nan 0.1000 1.7786
## 20 4.6624 nan 0.1000 0.2582
## 40 2.5818 nan 0.1000 -0.0066
## 60 1.9679 nan 0.1000 -0.0325
## 80 1.6002 nan 0.1000 -0.0226
## 100 1.3540 nan 0.1000 -0.0161
## 120 1.1498 nan 0.1000 -0.0136
## 140 0.9919 nan 0.1000 -0.0267
## 160 0.8499 nan 0.1000 -0.0157
## 180 0.7349 nan 0.1000 -0.0061
## 200 0.6403 nan 0.1000 -0.0099
## 220 0.5614 nan 0.1000 -0.0153
## 240 0.5055 nan 0.1000 -0.0072
## 260 0.4534 nan 0.1000 -0.0073
## 280 0.4083 nan 0.1000 -0.0065
## 300 0.3733 nan 0.1000 -0.0071
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.3284 nan 0.1000 10.0724
## 2 43.5374 nan 0.1000 8.9578
## 3 36.9045 nan 0.1000 6.5754
## 4 31.3420 nan 0.1000 5.9663
## 5 26.6040 nan 0.1000 4.1599
## 6 22.5944 nan 0.1000 3.6266
## 7 19.2239 nan 0.1000 3.1525
## 8 16.4007 nan 0.1000 3.0384
## 9 14.1583 nan 0.1000 2.1247
## 10 12.3126 nan 0.1000 1.8650
## 20 4.5941 nan 0.1000 0.2321
## 40 2.6499 nan 0.1000 -0.0497
## 60 2.2690 nan 0.1000 -0.0326
## 80 1.9554 nan 0.1000 -0.0047
## 100 1.7331 nan 0.1000 -0.0390
## 120 1.5406 nan 0.1000 -0.0152
## 140 1.3897 nan 0.1000 -0.0130
## 160 1.2579 nan 0.1000 -0.0224
## 180 1.1366 nan 0.1000 -0.0260
## 200 1.0442 nan 0.1000 -0.0227
## 220 0.9432 nan 0.1000 -0.0127
## 240 0.8643 nan 0.1000 -0.0111
## 260 0.7949 nan 0.1000 -0.0077
## 280 0.7388 nan 0.1000 -0.0090
## 300 0.6876 nan 0.1000 -0.0085
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.8004 nan 0.1000 10.4799
## 2 43.9093 nan 0.1000 8.7984
## 3 36.8988 nan 0.1000 7.5465
## 4 31.4247 nan 0.1000 5.5004
## 5 26.8221 nan 0.1000 4.8386
## 6 22.7944 nan 0.1000 3.6298
## 7 19.6499 nan 0.1000 2.9841
## 8 17.0872 nan 0.1000 2.5629
## 9 15.0596 nan 0.1000 1.7730
## 10 13.2765 nan 0.1000 1.8008
## 20 5.2457 nan 0.1000 0.2893
## 40 3.1964 nan 0.1000 -0.0233
## 60 2.7338 nan 0.1000 -0.0321
## 80 2.4244 nan 0.1000 -0.0454
## 100 2.1530 nan 0.1000 -0.0122
## 120 1.9598 nan 0.1000 -0.0248
## 140 1.7813 nan 0.1000 -0.0071
## 160 1.6445 nan 0.1000 -0.0306
## 180 1.5270 nan 0.1000 -0.0250
## 200 1.4495 nan 0.1000 -0.0274
## 220 1.3159 nan 0.1000 -0.0140
## 240 1.2103 nan 0.1000 -0.0149
## 260 1.1202 nan 0.1000 -0.0155
## 280 1.0456 nan 0.1000 -0.0115
## 300 0.9960 nan 0.1000 -0.0219
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.9368 nan 0.0100 0.6958
## 2 58.2282 nan 0.0100 0.6959
## 3 57.5163 nan 0.0100 0.6871
## 4 56.8017 nan 0.0100 0.7084
## 5 56.0913 nan 0.0100 0.6493
## 6 55.3699 nan 0.0100 0.6966
## 7 54.7054 nan 0.0100 0.7111
## 8 54.0419 nan 0.0100 0.7034
## 9 53.4059 nan 0.0100 0.6037
## 10 52.7683 nan 0.0100 0.5773
## 20 46.7715 nan 0.0100 0.4481
## 40 37.7370 nan 0.0100 0.3473
## 60 30.6736 nan 0.0100 0.2820
## 80 25.3878 nan 0.0100 0.2176
## 100 21.3613 nan 0.0100 0.1826
## 120 18.2616 nan 0.0100 0.1124
## 140 15.8383 nan 0.0100 0.0775
## 160 13.8920 nan 0.0100 0.0826
## 180 12.3446 nan 0.0100 0.0589
## 200 11.0718 nan 0.0100 0.0580
## 220 9.9955 nan 0.0100 0.0406
## 240 9.0836 nan 0.0100 0.0363
## 260 8.3120 nan 0.0100 0.0118
## 280 7.6393 nan 0.0100 0.0271
## 300 7.0795 nan 0.0100 0.0251
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.9345 nan 0.0100 0.7421
## 2 58.1976 nan 0.0100 0.7083
## 3 57.4544 nan 0.0100 0.7288
## 4 56.7585 nan 0.0100 0.7630
## 5 56.0923 nan 0.0100 0.6877
## 6 55.3795 nan 0.0100 0.6640
## 7 54.7033 nan 0.0100 0.6739
## 8 54.0282 nan 0.0100 0.6526
## 9 53.4135 nan 0.0100 0.6435
## 10 52.7848 nan 0.0100 0.6709
## 20 47.0669 nan 0.0100 0.5421
## 40 38.0184 nan 0.0100 0.4092
## 60 30.9632 nan 0.0100 0.2853
## 80 25.7020 nan 0.0100 0.2138
## 100 21.6669 nan 0.0100 0.1600
## 120 18.4841 nan 0.0100 0.1230
## 140 16.0269 nan 0.0100 0.0938
## 160 14.0636 nan 0.0100 0.0904
## 180 12.4889 nan 0.0100 0.0623
## 200 11.1804 nan 0.0100 0.0535
## 220 10.0825 nan 0.0100 0.0487
## 240 9.1349 nan 0.0100 0.0537
## 260 8.3593 nan 0.0100 0.0275
## 280 7.7144 nan 0.0100 0.0223
## 300 7.1423 nan 0.0100 0.0184
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.8447 nan 0.0100 0.7519
## 2 58.1389 nan 0.0100 0.7079
## 3 57.4214 nan 0.0100 0.7265
## 4 56.6957 nan 0.0100 0.7188
## 5 55.9591 nan 0.0100 0.7129
## 6 55.2185 nan 0.0100 0.7421
## 7 54.5820 nan 0.0100 0.6987
## 8 53.9502 nan 0.0100 0.6532
## 9 53.2727 nan 0.0100 0.6037
## 10 52.7122 nan 0.0100 0.5277
## 20 47.0099 nan 0.0100 0.5173
## 40 37.9019 nan 0.0100 0.3892
## 60 31.0371 nan 0.0100 0.2787
## 80 25.7268 nan 0.0100 0.2188
## 100 21.6772 nan 0.0100 0.1497
## 120 18.5327 nan 0.0100 0.1388
## 140 16.1403 nan 0.0100 0.0981
## 160 14.1824 nan 0.0100 0.0732
## 180 12.5550 nan 0.0100 0.0619
## 200 11.1649 nan 0.0100 0.0580
## 220 10.0838 nan 0.0100 0.0428
## 240 9.1792 nan 0.0100 0.0310
## 260 8.4321 nan 0.0100 0.0224
## 280 7.7832 nan 0.0100 0.0271
## 300 7.2327 nan 0.0100 0.0193
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.6534 nan 0.0100 1.0062
## 2 57.6516 nan 0.0100 0.9429
## 3 56.7025 nan 0.0100 0.9350
## 4 55.7856 nan 0.0100 0.9256
## 5 54.9393 nan 0.0100 0.9323
## 6 54.0453 nan 0.0100 0.8522
## 7 53.2068 nan 0.0100 0.9016
## 8 52.3138 nan 0.0100 0.8958
## 9 51.5176 nan 0.0100 0.7822
## 10 50.7209 nan 0.0100 0.8833
## 20 43.2229 nan 0.0100 0.6959
## 40 31.5793 nan 0.0100 0.4079
## 60 23.5262 nan 0.0100 0.3021
## 80 18.0266 nan 0.0100 0.1920
## 100 14.1394 nan 0.0100 0.1813
## 120 11.3292 nan 0.0100 0.1141
## 140 9.2786 nan 0.0100 0.0624
## 160 7.7394 nan 0.0100 0.0552
## 180 6.6288 nan 0.0100 0.0350
## 200 5.7798 nan 0.0100 0.0308
## 220 5.1309 nan 0.0100 0.0164
## 240 4.6390 nan 0.0100 0.0177
## 260 4.2494 nan 0.0100 0.0089
## 280 3.9517 nan 0.0100 0.0077
## 300 3.7216 nan 0.0100 0.0054
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.6537 nan 0.0100 0.9949
## 2 57.7285 nan 0.0100 0.8895
## 3 56.7543 nan 0.0100 0.9134
## 4 55.8228 nan 0.0100 1.0075
## 5 54.8791 nan 0.0100 0.8516
## 6 53.9636 nan 0.0100 0.8997
## 7 53.0311 nan 0.0100 0.9704
## 8 52.1466 nan 0.0100 0.9019
## 9 51.2903 nan 0.0100 0.7765
## 10 50.4514 nan 0.0100 0.8287
## 20 42.9765 nan 0.0100 0.6812
## 40 31.4786 nan 0.0100 0.5159
## 60 23.5451 nan 0.0100 0.3427
## 80 17.9372 nan 0.0100 0.1988
## 100 14.0338 nan 0.0100 0.1616
## 120 11.3038 nan 0.0100 0.1158
## 140 9.2408 nan 0.0100 0.0710
## 160 7.7725 nan 0.0100 0.0555
## 180 6.6545 nan 0.0100 0.0426
## 200 5.7857 nan 0.0100 0.0283
## 220 5.1513 nan 0.0100 0.0212
## 240 4.6650 nan 0.0100 0.0137
## 260 4.3122 nan 0.0100 0.0116
## 280 4.0165 nan 0.0100 0.0088
## 300 3.8111 nan 0.0100 0.0060
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.6552 nan 0.0100 0.9249
## 2 57.6478 nan 0.0100 1.0111
## 3 56.6581 nan 0.0100 1.0011
## 4 55.7095 nan 0.0100 0.9740
## 5 54.7816 nan 0.0100 0.9540
## 6 53.8327 nan 0.0100 1.0074
## 7 52.9336 nan 0.0100 0.8186
## 8 52.0684 nan 0.0100 0.7978
## 9 51.2438 nan 0.0100 0.8293
## 10 50.4099 nan 0.0100 0.7810
## 20 42.9934 nan 0.0100 0.6995
## 40 31.6879 nan 0.0100 0.4722
## 60 23.7871 nan 0.0100 0.3308
## 80 18.2452 nan 0.0100 0.2423
## 100 14.4221 nan 0.0100 0.1412
## 120 11.6408 nan 0.0100 0.0893
## 140 9.6140 nan 0.0100 0.0632
## 160 8.1201 nan 0.0100 0.0480
## 180 6.9933 nan 0.0100 0.0473
## 200 6.1425 nan 0.0100 0.0244
## 220 5.4989 nan 0.0100 0.0263
## 240 5.0032 nan 0.0100 0.0205
## 260 4.6434 nan 0.0100 0.0101
## 280 4.3591 nan 0.0100 0.0067
## 300 4.1404 nan 0.0100 0.0028
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.6215 nan 0.0100 0.9360
## 2 57.5973 nan 0.0100 1.0649
## 3 56.5718 nan 0.0100 0.9783
## 4 55.6346 nan 0.0100 0.9597
## 5 54.6489 nan 0.0100 0.9505
## 6 53.6826 nan 0.0100 0.8583
## 7 52.7759 nan 0.0100 0.8184
## 8 51.8215 nan 0.0100 0.8624
## 9 50.8656 nan 0.0100 0.9234
## 10 49.9881 nan 0.0100 0.8717
## 20 42.0980 nan 0.0100 0.7069
## 40 30.0272 nan 0.0100 0.4499
## 60 21.8002 nan 0.0100 0.3024
## 80 16.3335 nan 0.0100 0.1964
## 100 12.4237 nan 0.0100 0.1491
## 120 9.7270 nan 0.0100 0.1098
## 140 7.8229 nan 0.0100 0.0581
## 160 6.4332 nan 0.0100 0.0460
## 180 5.4406 nan 0.0100 0.0214
## 200 4.7048 nan 0.0100 0.0166
## 220 4.1639 nan 0.0100 0.0161
## 240 3.7474 nan 0.0100 0.0091
## 260 3.4471 nan 0.0100 0.0060
## 280 3.2346 nan 0.0100 -0.0014
## 300 3.0543 nan 0.0100 0.0030
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.6047 nan 0.0100 0.9812
## 2 57.6138 nan 0.0100 1.0773
## 3 56.5962 nan 0.0100 0.9424
## 4 55.5893 nan 0.0100 0.8490
## 5 54.5629 nan 0.0100 0.8462
## 6 53.5969 nan 0.0100 0.9303
## 7 52.6481 nan 0.0100 0.8918
## 8 51.7491 nan 0.0100 0.9577
## 9 50.8389 nan 0.0100 0.9734
## 10 49.9636 nan 0.0100 0.8165
## 20 42.1400 nan 0.0100 0.6951
## 40 30.2455 nan 0.0100 0.4716
## 60 22.0958 nan 0.0100 0.3687
## 80 16.4790 nan 0.0100 0.2389
## 100 12.4753 nan 0.0100 0.1586
## 120 9.7095 nan 0.0100 0.1091
## 140 7.7975 nan 0.0100 0.0659
## 160 6.4313 nan 0.0100 0.0449
## 180 5.4768 nan 0.0100 0.0254
## 200 4.7724 nan 0.0100 0.0253
## 220 4.2325 nan 0.0100 0.0134
## 240 3.8623 nan 0.0100 0.0124
## 260 3.5654 nan 0.0100 0.0033
## 280 3.3369 nan 0.0100 0.0006
## 300 3.1697 nan 0.0100 0.0020
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.6271 nan 0.0100 0.8861
## 2 57.5992 nan 0.0100 1.0159
## 3 56.5539 nan 0.0100 1.1194
## 4 55.6044 nan 0.0100 0.9860
## 5 54.6918 nan 0.0100 0.8781
## 6 53.7361 nan 0.0100 0.9336
## 7 52.8073 nan 0.0100 0.7909
## 8 51.9111 nan 0.0100 0.7630
## 9 51.0111 nan 0.0100 0.8385
## 10 50.1078 nan 0.0100 0.9061
## 20 42.2308 nan 0.0100 0.6613
## 40 30.3481 nan 0.0100 0.4266
## 60 22.1665 nan 0.0100 0.3193
## 80 16.5886 nan 0.0100 0.2666
## 100 12.8261 nan 0.0100 0.1298
## 120 10.1455 nan 0.0100 0.0981
## 140 8.2573 nan 0.0100 0.0791
## 160 6.9054 nan 0.0100 0.0444
## 180 5.9115 nan 0.0100 0.0329
## 200 5.1973 nan 0.0100 0.0261
## 220 4.6745 nan 0.0100 0.0171
## 240 4.2928 nan 0.0100 0.0069
## 260 4.0051 nan 0.0100 0.0054
## 280 3.7803 nan 0.0100 0.0014
## 300 3.6225 nan 0.0100 0.0025
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.7455 nan 0.0500 3.9885
## 2 52.3909 nan 0.0500 3.4541
## 3 49.6441 nan 0.0500 2.4246
## 4 46.8784 nan 0.0500 2.8210
## 5 44.2155 nan 0.0500 2.5158
## 6 41.7622 nan 0.0500 2.2067
## 7 39.6512 nan 0.0500 2.1829
## 8 37.6377 nan 0.0500 1.6979
## 9 35.6221 nan 0.0500 2.1787
## 10 33.7747 nan 0.0500 1.6337
## 20 21.4593 nan 0.0500 0.8620
## 40 11.1655 nan 0.0500 0.2403
## 60 6.9941 nan 0.0500 0.1245
## 80 5.2472 nan 0.0500 0.0057
## 100 4.3751 nan 0.0500 0.0243
## 120 3.9301 nan 0.0500 -0.0243
## 140 3.7095 nan 0.0500 -0.0018
## 160 3.6022 nan 0.0500 0.0036
## 180 3.5058 nan 0.0500 -0.0025
## 200 3.4432 nan 0.0500 -0.0149
## 220 3.3800 nan 0.0500 -0.0012
## 240 3.3194 nan 0.0500 -0.0089
## 260 3.2774 nan 0.0500 -0.0240
## 280 3.2239 nan 0.0500 -0.0091
## 300 3.1706 nan 0.0500 0.0016
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.1470 nan 0.0500 3.6240
## 2 52.7097 nan 0.0500 3.6194
## 3 49.8643 nan 0.0500 2.3946
## 4 46.7450 nan 0.0500 2.8934
## 5 44.2464 nan 0.0500 2.4908
## 6 41.6984 nan 0.0500 2.2906
## 7 39.5403 nan 0.0500 2.3850
## 8 37.4274 nan 0.0500 2.0538
## 9 35.4990 nan 0.0500 1.8143
## 10 33.5412 nan 0.0500 1.7861
## 20 21.2077 nan 0.0500 0.7627
## 40 10.8542 nan 0.0500 0.2377
## 60 7.0048 nan 0.0500 0.0641
## 80 5.2486 nan 0.0500 0.0531
## 100 4.4565 nan 0.0500 0.0244
## 120 4.0000 nan 0.0500 0.0092
## 140 3.8053 nan 0.0500 -0.0192
## 160 3.6794 nan 0.0500 -0.0194
## 180 3.5938 nan 0.0500 -0.0015
## 200 3.5247 nan 0.0500 -0.0008
## 220 3.4575 nan 0.0500 -0.0005
## 240 3.3980 nan 0.0500 -0.0083
## 260 3.3367 nan 0.0500 -0.0058
## 280 3.2924 nan 0.0500 -0.0107
## 300 3.2441 nan 0.0500 -0.0063
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.8003 nan 0.0500 3.6979
## 2 52.4842 nan 0.0500 3.2202
## 3 49.3210 nan 0.0500 2.9657
## 4 46.8059 nan 0.0500 2.1259
## 5 44.2487 nan 0.0500 2.7197
## 6 41.7047 nan 0.0500 2.2589
## 7 39.4682 nan 0.0500 2.1230
## 8 37.2456 nan 0.0500 2.1317
## 9 35.4520 nan 0.0500 1.9088
## 10 33.7465 nan 0.0500 1.6150
## 20 21.8730 nan 0.0500 0.9580
## 40 11.1305 nan 0.0500 0.3069
## 60 7.1215 nan 0.0500 0.1033
## 80 5.3705 nan 0.0500 0.0294
## 100 4.5417 nan 0.0500 0.0160
## 120 4.1591 nan 0.0500 -0.0060
## 140 3.9792 nan 0.0500 0.0003
## 160 3.8654 nan 0.0500 -0.0049
## 180 3.7677 nan 0.0500 -0.0092
## 200 3.6709 nan 0.0500 -0.0112
## 220 3.5893 nan 0.0500 -0.0045
## 240 3.5239 nan 0.0500 -0.0071
## 260 3.4592 nan 0.0500 -0.0048
## 280 3.4050 nan 0.0500 -0.0089
## 300 3.3521 nan 0.0500 -0.0084
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.6431 nan 0.0500 4.9729
## 2 50.3649 nan 0.0500 4.1650
## 3 46.4633 nan 0.0500 3.9962
## 4 42.6439 nan 0.0500 3.4716
## 5 39.1465 nan 0.0500 3.0613
## 6 36.1308 nan 0.0500 3.1025
## 7 33.2877 nan 0.0500 2.2693
## 8 30.8152 nan 0.0500 2.6181
## 9 28.6140 nan 0.0500 1.9878
## 10 26.5724 nan 0.0500 1.9819
## 20 13.7432 nan 0.0500 0.9163
## 40 5.7383 nan 0.0500 0.1554
## 60 3.7928 nan 0.0500 0.0238
## 80 3.1485 nan 0.0500 -0.0034
## 100 2.8603 nan 0.0500 -0.0127
## 120 2.6383 nan 0.0500 -0.0008
## 140 2.4793 nan 0.0500 -0.0328
## 160 2.3070 nan 0.0500 -0.0274
## 180 2.1608 nan 0.0500 -0.0084
## 200 2.0373 nan 0.0500 -0.0014
## 220 1.9326 nan 0.0500 -0.0114
## 240 1.8379 nan 0.0500 -0.0115
## 260 1.7367 nan 0.0500 -0.0045
## 280 1.6568 nan 0.0500 -0.0048
## 300 1.5973 nan 0.0500 -0.0081
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.0196 nan 0.0500 4.6119
## 2 50.8345 nan 0.0500 4.1510
## 3 46.7612 nan 0.0500 3.7373
## 4 43.0339 nan 0.0500 3.1780
## 5 39.8748 nan 0.0500 3.0077
## 6 36.6364 nan 0.0500 2.9471
## 7 33.8056 nan 0.0500 2.5013
## 8 31.2879 nan 0.0500 2.3027
## 9 28.9631 nan 0.0500 1.8076
## 10 26.9609 nan 0.0500 2.0095
## 20 13.8211 nan 0.0500 0.8349
## 40 5.8828 nan 0.0500 0.0954
## 60 3.8975 nan 0.0500 0.0086
## 80 3.3065 nan 0.0500 -0.0112
## 100 3.0233 nan 0.0500 -0.0038
## 120 2.8387 nan 0.0500 -0.0094
## 140 2.6716 nan 0.0500 -0.0180
## 160 2.5129 nan 0.0500 -0.0338
## 180 2.4064 nan 0.0500 -0.0118
## 200 2.2939 nan 0.0500 -0.0132
## 220 2.1910 nan 0.0500 -0.0083
## 240 2.1030 nan 0.0500 -0.0186
## 260 2.0391 nan 0.0500 -0.0136
## 280 1.9786 nan 0.0500 -0.0257
## 300 1.9144 nan 0.0500 -0.0086
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.6565 nan 0.0500 4.9930
## 2 50.0634 nan 0.0500 4.2518
## 3 45.9514 nan 0.0500 3.4755
## 4 42.2227 nan 0.0500 3.5463
## 5 38.9535 nan 0.0500 2.9727
## 6 36.0928 nan 0.0500 3.0717
## 7 33.4569 nan 0.0500 2.7614
## 8 30.8881 nan 0.0500 2.3115
## 9 28.7637 nan 0.0500 2.1251
## 10 26.7774 nan 0.0500 2.0122
## 20 14.4285 nan 0.0500 0.8656
## 40 6.1414 nan 0.0500 0.1275
## 60 4.1800 nan 0.0500 0.0202
## 80 3.6274 nan 0.0500 -0.0161
## 100 3.3072 nan 0.0500 -0.0053
## 120 3.1024 nan 0.0500 -0.0137
## 140 2.9503 nan 0.0500 -0.0127
## 160 2.8079 nan 0.0500 -0.0123
## 180 2.6939 nan 0.0500 -0.0130
## 200 2.5996 nan 0.0500 -0.0085
## 220 2.5037 nan 0.0500 -0.0212
## 240 2.4287 nan 0.0500 -0.0105
## 260 2.3524 nan 0.0500 -0.0111
## 280 2.2838 nan 0.0500 -0.0182
## 300 2.2256 nan 0.0500 -0.0294
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.6937 nan 0.0500 4.5151
## 2 50.4994 nan 0.0500 4.4749
## 3 46.0594 nan 0.0500 4.6181
## 4 42.2246 nan 0.0500 3.7210
## 5 38.6145 nan 0.0500 3.3517
## 6 35.4594 nan 0.0500 3.0589
## 7 32.5716 nan 0.0500 3.0667
## 8 30.0517 nan 0.0500 2.5425
## 9 27.8618 nan 0.0500 2.2512
## 10 25.6538 nan 0.0500 2.1904
## 20 12.3599 nan 0.0500 0.9173
## 40 4.6041 nan 0.0500 0.0833
## 60 2.9897 nan 0.0500 -0.0045
## 80 2.4872 nan 0.0500 -0.0106
## 100 2.1966 nan 0.0500 -0.0273
## 120 1.9864 nan 0.0500 -0.0176
## 140 1.7963 nan 0.0500 -0.0179
## 160 1.6543 nan 0.0500 -0.0153
## 180 1.5219 nan 0.0500 -0.0010
## 200 1.3963 nan 0.0500 -0.0133
## 220 1.2837 nan 0.0500 -0.0061
## 240 1.1931 nan 0.0500 -0.0131
## 260 1.1063 nan 0.0500 -0.0039
## 280 1.0308 nan 0.0500 -0.0109
## 300 0.9593 nan 0.0500 -0.0099
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.3349 nan 0.0500 4.6946
## 2 49.6219 nan 0.0500 5.1064
## 3 45.5924 nan 0.0500 3.8907
## 4 41.9220 nan 0.0500 3.3193
## 5 38.6614 nan 0.0500 3.4101
## 6 35.5781 nan 0.0500 3.2242
## 7 32.5910 nan 0.0500 3.0844
## 8 30.0082 nan 0.0500 2.4565
## 9 27.6492 nan 0.0500 2.1421
## 10 25.5429 nan 0.0500 2.2653
## 20 12.1288 nan 0.0500 0.7503
## 40 4.6470 nan 0.0500 0.1056
## 60 3.1391 nan 0.0500 0.0011
## 80 2.6168 nan 0.0500 -0.0160
## 100 2.3787 nan 0.0500 -0.0212
## 120 2.1827 nan 0.0500 -0.0093
## 140 2.0216 nan 0.0500 -0.0102
## 160 1.8625 nan 0.0500 -0.0148
## 180 1.7366 nan 0.0500 -0.0176
## 200 1.6237 nan 0.0500 -0.0088
## 220 1.5395 nan 0.0500 -0.0069
## 240 1.4601 nan 0.0500 -0.0049
## 260 1.3888 nan 0.0500 -0.0097
## 280 1.3241 nan 0.0500 -0.0140
## 300 1.2637 nan 0.0500 -0.0151
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.5929 nan 0.0500 5.4908
## 2 49.9664 nan 0.0500 3.9270
## 3 45.7726 nan 0.0500 4.1623
## 4 41.9654 nan 0.0500 3.7929
## 5 38.6443 nan 0.0500 3.4393
## 6 35.4581 nan 0.0500 3.0428
## 7 32.7175 nan 0.0500 2.7409
## 8 30.2537 nan 0.0500 2.5740
## 9 28.0862 nan 0.0500 2.2820
## 10 26.0135 nan 0.0500 1.9770
## 20 12.5471 nan 0.0500 0.8101
## 40 5.1348 nan 0.0500 0.1186
## 60 3.6932 nan 0.0500 0.0164
## 80 3.2272 nan 0.0500 -0.0119
## 100 2.9629 nan 0.0500 -0.0061
## 120 2.7384 nan 0.0500 -0.0190
## 140 2.5361 nan 0.0500 -0.0130
## 160 2.3896 nan 0.0500 -0.0201
## 180 2.2547 nan 0.0500 -0.0141
## 200 2.1351 nan 0.0500 -0.0123
## 220 2.0283 nan 0.0500 -0.0114
## 240 1.9462 nan 0.0500 -0.0106
## 260 1.8627 nan 0.0500 -0.0144
## 280 1.7964 nan 0.0500 -0.0110
## 300 1.7311 nan 0.0500 -0.0090
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.9839 nan 0.1000 6.9754
## 2 47.1768 nan 0.1000 5.6638
## 3 42.1405 nan 0.1000 4.9206
## 4 37.9827 nan 0.1000 4.2613
## 5 33.9113 nan 0.1000 3.1185
## 6 30.4651 nan 0.1000 3.3412
## 7 27.7503 nan 0.1000 2.7547
## 8 24.9550 nan 0.1000 2.5442
## 9 22.8597 nan 0.1000 2.0980
## 10 20.8382 nan 0.1000 1.8342
## 20 10.9077 nan 0.1000 0.4873
## 40 5.1754 nan 0.1000 0.1255
## 60 3.9780 nan 0.1000 0.0361
## 80 3.6496 nan 0.1000 -0.0010
## 100 3.5213 nan 0.1000 -0.0211
## 120 3.3883 nan 0.1000 -0.0142
## 140 3.3020 nan 0.1000 -0.0138
## 160 3.2203 nan 0.1000 -0.0216
## 180 3.1605 nan 0.1000 -0.0152
## 200 3.0923 nan 0.1000 -0.0198
## 220 3.0479 nan 0.1000 -0.0279
## 240 2.9893 nan 0.1000 -0.0241
## 260 2.9363 nan 0.1000 -0.0234
## 280 2.8916 nan 0.1000 -0.0058
## 300 2.8653 nan 0.1000 -0.0224
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.4362 nan 0.1000 7.0030
## 2 46.6116 nan 0.1000 5.6361
## 3 42.0642 nan 0.1000 4.5335
## 4 37.7826 nan 0.1000 4.1554
## 5 33.8582 nan 0.1000 3.5498
## 6 30.6153 nan 0.1000 3.1498
## 7 27.8829 nan 0.1000 2.6934
## 8 25.2753 nan 0.1000 2.4935
## 9 23.1285 nan 0.1000 1.8595
## 10 20.9749 nan 0.1000 1.8857
## 20 10.9571 nan 0.1000 0.6122
## 40 5.3502 nan 0.1000 0.0779
## 60 4.1763 nan 0.1000 -0.0137
## 80 3.8024 nan 0.1000 0.0007
## 100 3.6066 nan 0.1000 -0.0004
## 120 3.4560 nan 0.1000 -0.0168
## 140 3.3229 nan 0.1000 -0.0074
## 160 3.2409 nan 0.1000 -0.0217
## 180 3.1511 nan 0.1000 -0.0155
## 200 3.0869 nan 0.1000 -0.0089
## 220 3.0271 nan 0.1000 -0.0201
## 240 2.9514 nan 0.1000 -0.0077
## 260 2.8977 nan 0.1000 -0.0229
## 280 2.8568 nan 0.1000 -0.0198
## 300 2.8289 nan 0.1000 -0.0302
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.0270 nan 0.1000 6.8942
## 2 46.1065 nan 0.1000 5.8935
## 3 41.0751 nan 0.1000 4.6510
## 4 36.9179 nan 0.1000 3.5711
## 5 32.7213 nan 0.1000 4.1847
## 6 30.0405 nan 0.1000 2.7369
## 7 27.1135 nan 0.1000 3.0482
## 8 24.6044 nan 0.1000 2.2309
## 9 22.4807 nan 0.1000 1.8250
## 10 20.5759 nan 0.1000 1.5399
## 20 11.0405 nan 0.1000 0.6537
## 40 5.4706 nan 0.1000 0.0742
## 60 4.4475 nan 0.1000 -0.0146
## 80 4.1196 nan 0.1000 -0.0074
## 100 3.9276 nan 0.1000 -0.0098
## 120 3.7744 nan 0.1000 -0.0933
## 140 3.6593 nan 0.1000 -0.0394
## 160 3.5600 nan 0.1000 -0.0037
## 180 3.4577 nan 0.1000 -0.0154
## 200 3.3640 nan 0.1000 -0.0251
## 220 3.2994 nan 0.1000 -0.0099
## 240 3.2379 nan 0.1000 -0.0178
## 260 3.1911 nan 0.1000 -0.0169
## 280 3.1438 nan 0.1000 -0.0178
## 300 3.0936 nan 0.1000 -0.0188
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.3739 nan 0.1000 9.3698
## 2 42.4066 nan 0.1000 7.9101
## 3 35.9464 nan 0.1000 6.1595
## 4 30.7062 nan 0.1000 4.9506
## 5 26.4514 nan 0.1000 4.3995
## 6 22.7113 nan 0.1000 3.8440
## 7 19.7809 nan 0.1000 2.9080
## 8 17.1170 nan 0.1000 2.4644
## 9 15.0812 nan 0.1000 1.8732
## 10 13.5011 nan 0.1000 1.6270
## 20 5.6571 nan 0.1000 0.3023
## 40 3.2089 nan 0.1000 0.0003
## 60 2.7183 nan 0.1000 -0.1074
## 80 2.3969 nan 0.1000 -0.0318
## 100 2.1714 nan 0.1000 -0.0427
## 120 1.9679 nan 0.1000 -0.0297
## 140 1.7991 nan 0.1000 -0.0175
## 160 1.6459 nan 0.1000 -0.0251
## 180 1.5194 nan 0.1000 -0.0260
## 200 1.4013 nan 0.1000 -0.0079
## 220 1.3234 nan 0.1000 -0.0060
## 240 1.2112 nan 0.1000 -0.0197
## 260 1.1298 nan 0.1000 -0.0158
## 280 1.0584 nan 0.1000 -0.0079
## 300 0.9908 nan 0.1000 -0.0168
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.1423 nan 0.1000 9.6209
## 2 42.2315 nan 0.1000 7.6145
## 3 35.5985 nan 0.1000 6.9319
## 4 30.6417 nan 0.1000 4.9830
## 5 26.4719 nan 0.1000 4.1577
## 6 23.1671 nan 0.1000 3.0891
## 7 20.0749 nan 0.1000 2.8474
## 8 17.4301 nan 0.1000 2.4057
## 9 15.2617 nan 0.1000 2.2254
## 10 13.4821 nan 0.1000 1.4544
## 20 5.7217 nan 0.1000 0.2402
## 40 3.2569 nan 0.1000 0.0051
## 60 2.8185 nan 0.1000 -0.0257
## 80 2.4921 nan 0.1000 -0.0244
## 100 2.2173 nan 0.1000 -0.0101
## 120 2.0746 nan 0.1000 -0.0171
## 140 1.9363 nan 0.1000 -0.0171
## 160 1.8394 nan 0.1000 -0.0283
## 180 1.7516 nan 0.1000 -0.0347
## 200 1.6615 nan 0.1000 -0.0339
## 220 1.5769 nan 0.1000 -0.0080
## 240 1.4851 nan 0.1000 -0.0231
## 260 1.4077 nan 0.1000 -0.0137
## 280 1.3438 nan 0.1000 -0.0256
## 300 1.2647 nan 0.1000 -0.0199
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.4355 nan 0.1000 9.1791
## 2 42.7613 nan 0.1000 8.2567
## 3 36.2901 nan 0.1000 6.3323
## 4 30.8923 nan 0.1000 4.7599
## 5 26.5866 nan 0.1000 3.7903
## 6 22.9649 nan 0.1000 3.4182
## 7 19.7538 nan 0.1000 2.9222
## 8 17.4859 nan 0.1000 2.0773
## 9 15.3692 nan 0.1000 1.5229
## 10 13.7406 nan 0.1000 1.5391
## 20 5.9899 nan 0.1000 0.1866
## 40 3.6544 nan 0.1000 -0.0130
## 60 3.1821 nan 0.1000 -0.0339
## 80 2.9197 nan 0.1000 -0.0033
## 100 2.7338 nan 0.1000 -0.0081
## 120 2.5220 nan 0.1000 -0.0506
## 140 2.4259 nan 0.1000 -0.0428
## 160 2.3044 nan 0.1000 -0.0218
## 180 2.1652 nan 0.1000 -0.0086
## 200 2.0658 nan 0.1000 -0.0306
## 220 1.9594 nan 0.1000 -0.0150
## 240 1.8706 nan 0.1000 -0.0244
## 260 1.7823 nan 0.1000 -0.0135
## 280 1.7086 nan 0.1000 -0.0217
## 300 1.6343 nan 0.1000 -0.0141
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.0358 nan 0.1000 9.6039
## 2 41.9071 nan 0.1000 8.7358
## 3 35.3512 nan 0.1000 6.0948
## 4 29.8506 nan 0.1000 5.4915
## 5 24.9486 nan 0.1000 4.1676
## 6 21.3502 nan 0.1000 3.7699
## 7 18.3742 nan 0.1000 3.0507
## 8 15.7390 nan 0.1000 2.5529
## 9 13.8129 nan 0.1000 2.2162
## 10 12.1372 nan 0.1000 1.7408
## 20 4.4674 nan 0.1000 0.3276
## 40 2.5738 nan 0.1000 -0.0508
## 60 2.0565 nan 0.1000 -0.0107
## 80 1.7035 nan 0.1000 -0.0131
## 100 1.4354 nan 0.1000 -0.0270
## 120 1.2558 nan 0.1000 -0.0128
## 140 1.0873 nan 0.1000 -0.0285
## 160 0.9588 nan 0.1000 -0.0188
## 180 0.8409 nan 0.1000 -0.0144
## 200 0.7396 nan 0.1000 -0.0125
## 220 0.6429 nan 0.1000 -0.0117
## 240 0.5773 nan 0.1000 -0.0071
## 260 0.5147 nan 0.1000 -0.0098
## 280 0.4687 nan 0.1000 -0.0086
## 300 0.4289 nan 0.1000 -0.0059
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 49.3781 nan 0.1000 10.2082
## 2 41.2664 nan 0.1000 8.5324
## 3 34.9080 nan 0.1000 6.8117
## 4 29.1983 nan 0.1000 6.2143
## 5 25.0246 nan 0.1000 4.3415
## 6 21.2041 nan 0.1000 3.5683
## 7 18.2219 nan 0.1000 2.8348
## 8 15.5300 nan 0.1000 2.5217
## 9 13.6710 nan 0.1000 1.8149
## 10 12.0531 nan 0.1000 1.5602
## 20 4.6794 nan 0.1000 0.2904
## 40 2.8004 nan 0.1000 0.0110
## 60 2.3400 nan 0.1000 -0.0204
## 80 2.0768 nan 0.1000 -0.0229
## 100 1.8216 nan 0.1000 -0.0276
## 120 1.6084 nan 0.1000 -0.0209
## 140 1.4537 nan 0.1000 -0.0200
## 160 1.3244 nan 0.1000 -0.0285
## 180 1.2165 nan 0.1000 -0.0214
## 200 1.1014 nan 0.1000 -0.0251
## 220 0.9935 nan 0.1000 -0.0131
## 240 0.9135 nan 0.1000 -0.0087
## 260 0.8482 nan 0.1000 -0.0224
## 280 0.7740 nan 0.1000 -0.0110
## 300 0.7205 nan 0.1000 -0.0115
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 49.8131 nan 0.1000 10.3738
## 2 41.1052 nan 0.1000 8.3125
## 3 34.7142 nan 0.1000 6.4450
## 4 29.3390 nan 0.1000 5.3870
## 5 25.2650 nan 0.1000 4.5349
## 6 21.6314 nan 0.1000 3.5284
## 7 18.6077 nan 0.1000 2.8149
## 8 16.1288 nan 0.1000 2.3862
## 9 14.0012 nan 0.1000 1.8217
## 10 12.3978 nan 0.1000 1.6263
## 20 5.2632 nan 0.1000 0.2041
## 40 3.2783 nan 0.1000 -0.0134
## 60 2.7730 nan 0.1000 -0.0550
## 80 2.3891 nan 0.1000 -0.0286
## 100 2.1456 nan 0.1000 -0.0409
## 120 2.0076 nan 0.1000 -0.0448
## 140 1.8535 nan 0.1000 -0.0250
## 160 1.7237 nan 0.1000 -0.0103
## 180 1.6003 nan 0.1000 -0.0247
## 200 1.5004 nan 0.1000 -0.0153
## 220 1.3819 nan 0.1000 -0.0124
## 240 1.2903 nan 0.1000 -0.0156
## 260 1.2056 nan 0.1000 -0.0210
## 280 1.1289 nan 0.1000 -0.0183
## 300 1.0644 nan 0.1000 -0.0251
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.8622 nan 0.0100 0.7858
## 2 60.1215 nan 0.0100 0.7612
## 3 59.3351 nan 0.0100 0.7847
## 4 58.5539 nan 0.0100 0.7021
## 5 57.7923 nan 0.0100 0.6952
## 6 57.0916 nan 0.0100 0.7035
## 7 56.4124 nan 0.0100 0.6949
## 8 55.7605 nan 0.0100 0.6829
## 9 55.0238 nan 0.0100 0.6909
## 10 54.3772 nan 0.0100 0.6815
## 20 48.1879 nan 0.0100 0.5441
## 40 38.8915 nan 0.0100 0.4001
## 60 31.7702 nan 0.0100 0.2475
## 80 26.2876 nan 0.0100 0.2742
## 100 22.2078 nan 0.0100 0.1748
## 120 18.8923 nan 0.0100 0.1349
## 140 16.3259 nan 0.0100 0.0843
## 160 14.2958 nan 0.0100 0.0954
## 180 12.6315 nan 0.0100 0.0591
## 200 11.2910 nan 0.0100 0.0561
## 220 10.1998 nan 0.0100 0.0392
## 240 9.2695 nan 0.0100 0.0391
## 260 8.4474 nan 0.0100 0.0347
## 280 7.7501 nan 0.0100 0.0161
## 300 7.1513 nan 0.0100 0.0146
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.8854 nan 0.0100 0.6969
## 2 60.1073 nan 0.0100 0.7310
## 3 59.3967 nan 0.0100 0.7999
## 4 58.6698 nan 0.0100 0.6938
## 5 57.9319 nan 0.0100 0.7599
## 6 57.2155 nan 0.0100 0.7338
## 7 56.4624 nan 0.0100 0.6651
## 8 55.7420 nan 0.0100 0.6485
## 9 55.0418 nan 0.0100 0.6859
## 10 54.3651 nan 0.0100 0.6285
## 20 48.4772 nan 0.0100 0.4640
## 40 39.0947 nan 0.0100 0.3918
## 60 32.0169 nan 0.0100 0.3185
## 80 26.5058 nan 0.0100 0.2158
## 100 22.3451 nan 0.0100 0.1622
## 120 19.1111 nan 0.0100 0.1380
## 140 16.4672 nan 0.0100 0.1122
## 160 14.4559 nan 0.0100 0.0706
## 180 12.7613 nan 0.0100 0.0576
## 200 11.4189 nan 0.0100 0.0582
## 220 10.2943 nan 0.0100 0.0521
## 240 9.3462 nan 0.0100 0.0483
## 260 8.5165 nan 0.0100 0.0336
## 280 7.8204 nan 0.0100 0.0320
## 300 7.2084 nan 0.0100 0.0127
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.8515 nan 0.0100 0.7710
## 2 60.0740 nan 0.0100 0.7560
## 3 59.3386 nan 0.0100 0.7130
## 4 58.5591 nan 0.0100 0.7299
## 5 57.7918 nan 0.0100 0.7060
## 6 57.0935 nan 0.0100 0.6704
## 7 56.3776 nan 0.0100 0.7286
## 8 55.6486 nan 0.0100 0.7283
## 9 54.9781 nan 0.0100 0.6806
## 10 54.3195 nan 0.0100 0.6347
## 20 48.4234 nan 0.0100 0.5273
## 40 38.8108 nan 0.0100 0.4193
## 60 31.8217 nan 0.0100 0.2951
## 80 26.3999 nan 0.0100 0.2231
## 100 22.2229 nan 0.0100 0.1660
## 120 18.9869 nan 0.0100 0.1055
## 140 16.5039 nan 0.0100 0.0999
## 160 14.4590 nan 0.0100 0.0799
## 180 12.8316 nan 0.0100 0.0526
## 200 11.5245 nan 0.0100 0.0490
## 220 10.3755 nan 0.0100 0.0390
## 240 9.4417 nan 0.0100 0.0358
## 260 8.6588 nan 0.0100 0.0264
## 280 8.0043 nan 0.0100 0.0232
## 300 7.4034 nan 0.0100 0.0221
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.6244 nan 0.0100 1.0422
## 2 59.5691 nan 0.0100 1.0235
## 3 58.5444 nan 0.0100 0.9633
## 4 57.5907 nan 0.0100 0.9335
## 5 56.6268 nan 0.0100 0.9290
## 6 55.6790 nan 0.0100 0.8467
## 7 54.7621 nan 0.0100 0.9628
## 8 53.8603 nan 0.0100 0.8882
## 9 53.0019 nan 0.0100 0.7640
## 10 52.1268 nan 0.0100 0.8619
## 20 44.2308 nan 0.0100 0.6923
## 40 32.6187 nan 0.0100 0.5073
## 60 24.4035 nan 0.0100 0.3343
## 80 18.6945 nan 0.0100 0.2406
## 100 14.5752 nan 0.0100 0.1588
## 120 11.6856 nan 0.0100 0.1103
## 140 9.5337 nan 0.0100 0.0979
## 160 7.8776 nan 0.0100 0.0680
## 180 6.6497 nan 0.0100 0.0488
## 200 5.7382 nan 0.0100 0.0406
## 220 5.0562 nan 0.0100 0.0282
## 240 4.5313 nan 0.0100 0.0166
## 260 4.1174 nan 0.0100 0.0002
## 280 3.7969 nan 0.0100 0.0108
## 300 3.5617 nan 0.0100 0.0087
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.6497 nan 0.0100 1.0361
## 2 59.6352 nan 0.0100 1.0197
## 3 58.6378 nan 0.0100 1.0019
## 4 57.6418 nan 0.0100 0.9638
## 5 56.6943 nan 0.0100 0.9908
## 6 55.7101 nan 0.0100 0.9005
## 7 54.7836 nan 0.0100 0.8536
## 8 53.9006 nan 0.0100 0.8389
## 9 53.0299 nan 0.0100 0.8221
## 10 52.1675 nan 0.0100 0.8277
## 20 44.4035 nan 0.0100 0.7242
## 40 32.6232 nan 0.0100 0.4740
## 60 24.3441 nan 0.0100 0.3631
## 80 18.5692 nan 0.0100 0.2625
## 100 14.5074 nan 0.0100 0.1387
## 120 11.5852 nan 0.0100 0.1115
## 140 9.4717 nan 0.0100 0.0752
## 160 7.8864 nan 0.0100 0.0677
## 180 6.6630 nan 0.0100 0.0281
## 200 5.7928 nan 0.0100 0.0306
## 220 5.1096 nan 0.0100 0.0206
## 240 4.6103 nan 0.0100 0.0175
## 260 4.2160 nan 0.0100 0.0154
## 280 3.9180 nan 0.0100 0.0018
## 300 3.6937 nan 0.0100 0.0024
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.6556 nan 0.0100 1.1317
## 2 59.6703 nan 0.0100 0.9472
## 3 58.7070 nan 0.0100 0.9769
## 4 57.7343 nan 0.0100 0.8783
## 5 56.7474 nan 0.0100 0.9332
## 6 55.8237 nan 0.0100 0.8892
## 7 54.9019 nan 0.0100 0.9539
## 8 54.0377 nan 0.0100 0.7506
## 9 53.1751 nan 0.0100 0.9145
## 10 52.3000 nan 0.0100 0.9598
## 20 44.4790 nan 0.0100 0.7267
## 40 32.7309 nan 0.0100 0.4784
## 60 24.4643 nan 0.0100 0.3220
## 80 18.7168 nan 0.0100 0.2286
## 100 14.7281 nan 0.0100 0.1624
## 120 11.8583 nan 0.0100 0.1210
## 140 9.7632 nan 0.0100 0.0894
## 160 8.1877 nan 0.0100 0.0588
## 180 7.0150 nan 0.0100 0.0585
## 200 6.0665 nan 0.0100 0.0416
## 220 5.3694 nan 0.0100 0.0266
## 240 4.8548 nan 0.0100 0.0183
## 260 4.4771 nan 0.0100 0.0124
## 280 4.1755 nan 0.0100 0.0083
## 300 3.9376 nan 0.0100 0.0031
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.5869 nan 0.0100 1.1631
## 2 59.5195 nan 0.0100 0.9420
## 3 58.4932 nan 0.0100 0.9698
## 4 57.4808 nan 0.0100 0.8713
## 5 56.4547 nan 0.0100 1.0556
## 6 55.4551 nan 0.0100 0.8990
## 7 54.4914 nan 0.0100 0.8823
## 8 53.5410 nan 0.0100 0.9121
## 9 52.5978 nan 0.0100 0.8866
## 10 51.7141 nan 0.0100 0.8587
## 20 43.5499 nan 0.0100 0.7624
## 40 31.1845 nan 0.0100 0.5068
## 60 22.7847 nan 0.0100 0.3511
## 80 16.9593 nan 0.0100 0.2376
## 100 12.8032 nan 0.0100 0.1515
## 120 9.8701 nan 0.0100 0.1090
## 140 7.8855 nan 0.0100 0.0831
## 160 6.3999 nan 0.0100 0.0507
## 180 5.3660 nan 0.0100 0.0320
## 200 4.5623 nan 0.0100 0.0250
## 220 3.9722 nan 0.0100 0.0157
## 240 3.5423 nan 0.0100 0.0107
## 260 3.2097 nan 0.0100 0.0054
## 280 2.9834 nan 0.0100 0.0038
## 300 2.7856 nan 0.0100 0.0005
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.5776 nan 0.0100 1.0296
## 2 59.4933 nan 0.0100 1.0543
## 3 58.4766 nan 0.0100 1.0763
## 4 57.4287 nan 0.0100 1.0608
## 5 56.4451 nan 0.0100 0.9021
## 6 55.4892 nan 0.0100 0.9716
## 7 54.5444 nan 0.0100 1.0167
## 8 53.6095 nan 0.0100 0.9541
## 9 52.6849 nan 0.0100 0.9316
## 10 51.7966 nan 0.0100 0.9580
## 20 43.5624 nan 0.0100 0.7522
## 40 31.1349 nan 0.0100 0.5174
## 60 22.7035 nan 0.0100 0.3389
## 80 16.8254 nan 0.0100 0.2484
## 100 12.8450 nan 0.0100 0.1585
## 120 9.9603 nan 0.0100 0.1072
## 140 7.9663 nan 0.0100 0.0834
## 160 6.5299 nan 0.0100 0.0553
## 180 5.4903 nan 0.0100 0.0396
## 200 4.7390 nan 0.0100 0.0211
## 220 4.1786 nan 0.0100 0.0247
## 240 3.7715 nan 0.0100 0.0042
## 260 3.4738 nan 0.0100 0.0031
## 280 3.2369 nan 0.0100 0.0088
## 300 3.0487 nan 0.0100 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.5996 nan 0.0100 0.8953
## 2 59.5606 nan 0.0100 1.1262
## 3 58.5221 nan 0.0100 0.9993
## 4 57.5063 nan 0.0100 1.0563
## 5 56.5369 nan 0.0100 0.9514
## 6 55.5624 nan 0.0100 1.0539
## 7 54.6471 nan 0.0100 0.9706
## 8 53.7201 nan 0.0100 0.8989
## 9 52.8280 nan 0.0100 0.9081
## 10 51.9032 nan 0.0100 0.8896
## 20 43.6068 nan 0.0100 0.7088
## 40 31.2918 nan 0.0100 0.5292
## 60 22.8699 nan 0.0100 0.3431
## 80 17.1338 nan 0.0100 0.2524
## 100 13.1551 nan 0.0100 0.1440
## 120 10.3012 nan 0.0100 0.1000
## 140 8.3062 nan 0.0100 0.0685
## 160 6.8389 nan 0.0100 0.0501
## 180 5.7844 nan 0.0100 0.0352
## 200 5.0482 nan 0.0100 0.0243
## 220 4.5270 nan 0.0100 0.0179
## 240 4.1464 nan 0.0100 0.0141
## 260 3.8453 nan 0.0100 0.0092
## 280 3.6180 nan 0.0100 0.0048
## 300 3.4441 nan 0.0100 0.0015
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.8659 nan 0.0500 3.8502
## 2 54.3191 nan 0.0500 2.9817
## 3 51.1178 nan 0.0500 2.9764
## 4 48.5175 nan 0.0500 2.6572
## 5 45.8940 nan 0.0500 2.7339
## 6 43.4309 nan 0.0500 2.4136
## 7 41.1371 nan 0.0500 2.0524
## 8 38.9164 nan 0.0500 1.9734
## 9 36.7036 nan 0.0500 1.9358
## 10 34.8032 nan 0.0500 1.6942
## 20 22.1542 nan 0.0500 0.8881
## 40 11.3766 nan 0.0500 0.2618
## 60 7.3514 nan 0.0500 0.1510
## 80 5.4349 nan 0.0500 0.0575
## 100 4.4834 nan 0.0500 0.0130
## 120 4.0175 nan 0.0500 -0.0022
## 140 3.7737 nan 0.0500 -0.0117
## 160 3.6121 nan 0.0500 -0.0094
## 180 3.4774 nan 0.0500 -0.0161
## 200 3.3913 nan 0.0500 -0.0053
## 220 3.3333 nan 0.0500 -0.0225
## 240 3.2593 nan 0.0500 -0.0095
## 260 3.2002 nan 0.0500 -0.0098
## 280 3.1515 nan 0.0500 -0.0057
## 300 3.1130 nan 0.0500 -0.0095
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.7882 nan 0.0500 3.9965
## 2 54.1363 nan 0.0500 3.3503
## 3 51.1976 nan 0.0500 2.8074
## 4 48.0151 nan 0.0500 2.9378
## 5 45.1334 nan 0.0500 2.6864
## 6 42.6746 nan 0.0500 2.1181
## 7 40.2941 nan 0.0500 2.0977
## 8 38.1431 nan 0.0500 1.9014
## 9 36.2302 nan 0.0500 1.8723
## 10 34.5503 nan 0.0500 1.5922
## 20 21.2147 nan 0.0500 0.8317
## 40 11.1262 nan 0.0500 0.2396
## 60 7.2280 nan 0.0500 0.1520
## 80 5.3339 nan 0.0500 0.0400
## 100 4.4383 nan 0.0500 0.0149
## 120 3.9916 nan 0.0500 0.0022
## 140 3.7708 nan 0.0500 -0.0056
## 160 3.6301 nan 0.0500 -0.0070
## 180 3.5340 nan 0.0500 -0.0038
## 200 3.4543 nan 0.0500 -0.0169
## 220 3.3869 nan 0.0500 -0.0061
## 240 3.3166 nan 0.0500 -0.0064
## 260 3.2699 nan 0.0500 -0.0217
## 280 3.2148 nan 0.0500 -0.0051
## 300 3.1730 nan 0.0500 -0.0054
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.1797 nan 0.0500 3.7733
## 2 54.3168 nan 0.0500 3.4353
## 3 51.3493 nan 0.0500 2.9450
## 4 48.3747 nan 0.0500 2.5359
## 5 45.7498 nan 0.0500 2.9776
## 6 43.3822 nan 0.0500 2.4471
## 7 40.9736 nan 0.0500 2.1355
## 8 38.6822 nan 0.0500 1.9300
## 9 36.8104 nan 0.0500 1.6827
## 10 34.7779 nan 0.0500 1.9006
## 20 22.1031 nan 0.0500 0.8688
## 40 11.2554 nan 0.0500 0.2471
## 60 7.3428 nan 0.0500 0.0969
## 80 5.5532 nan 0.0500 0.0223
## 100 4.7214 nan 0.0500 0.0034
## 120 4.3264 nan 0.0500 0.0089
## 140 4.0786 nan 0.0500 0.0054
## 160 3.9044 nan 0.0500 -0.0118
## 180 3.7743 nan 0.0500 -0.0014
## 200 3.6640 nan 0.0500 -0.0067
## 220 3.5821 nan 0.0500 -0.0008
## 240 3.5075 nan 0.0500 -0.0135
## 260 3.4452 nan 0.0500 -0.0052
## 280 3.3742 nan 0.0500 -0.0028
## 300 3.3140 nan 0.0500 -0.0089
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.6147 nan 0.0500 4.6856
## 2 51.9532 nan 0.0500 4.5843
## 3 47.8935 nan 0.0500 4.3724
## 4 44.1853 nan 0.0500 3.8858
## 5 40.7138 nan 0.0500 3.3027
## 6 37.6232 nan 0.0500 2.9641
## 7 34.9742 nan 0.0500 2.8070
## 8 32.3039 nan 0.0500 2.0199
## 9 30.1140 nan 0.0500 2.4929
## 10 27.9446 nan 0.0500 2.1338
## 20 14.7119 nan 0.0500 0.9035
## 40 5.7641 nan 0.0500 0.1531
## 60 3.5447 nan 0.0500 0.0286
## 80 2.8705 nan 0.0500 -0.0125
## 100 2.5672 nan 0.0500 -0.0001
## 120 2.3372 nan 0.0500 -0.0214
## 140 2.1622 nan 0.0500 -0.0159
## 160 2.0329 nan 0.0500 -0.0108
## 180 1.8908 nan 0.0500 -0.0082
## 200 1.7877 nan 0.0500 -0.0048
## 220 1.7013 nan 0.0500 -0.0177
## 240 1.6098 nan 0.0500 -0.0051
## 260 1.5338 nan 0.0500 -0.0086
## 280 1.4620 nan 0.0500 -0.0124
## 300 1.3867 nan 0.0500 -0.0073
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.7898 nan 0.0500 4.8951
## 2 52.0735 nan 0.0500 4.4419
## 3 48.0155 nan 0.0500 3.9285
## 4 44.4241 nan 0.0500 4.0645
## 5 40.9792 nan 0.0500 3.2920
## 6 37.9587 nan 0.0500 2.7786
## 7 35.1453 nan 0.0500 2.7625
## 8 32.5770 nan 0.0500 2.7282
## 9 30.1189 nan 0.0500 2.2010
## 10 27.9742 nan 0.0500 2.3167
## 20 14.4494 nan 0.0500 0.9264
## 40 5.7708 nan 0.0500 0.1227
## 60 3.7063 nan 0.0500 0.0164
## 80 3.1016 nan 0.0500 -0.0041
## 100 2.7879 nan 0.0500 -0.0016
## 120 2.5743 nan 0.0500 -0.0170
## 140 2.4015 nan 0.0500 -0.0064
## 160 2.2825 nan 0.0500 -0.0010
## 180 2.1594 nan 0.0500 -0.0077
## 200 2.0689 nan 0.0500 -0.0166
## 220 1.9922 nan 0.0500 -0.0074
## 240 1.9112 nan 0.0500 -0.0172
## 260 1.8217 nan 0.0500 -0.0100
## 280 1.7426 nan 0.0500 -0.0062
## 300 1.6689 nan 0.0500 -0.0057
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.8525 nan 0.0500 5.0353
## 2 52.2781 nan 0.0500 4.6157
## 3 48.2328 nan 0.0500 3.8441
## 4 44.6826 nan 0.0500 3.9135
## 5 41.1594 nan 0.0500 3.5338
## 6 38.2077 nan 0.0500 3.0143
## 7 35.3578 nan 0.0500 2.9199
## 8 32.7042 nan 0.0500 2.4874
## 9 30.3569 nan 0.0500 2.2613
## 10 28.2970 nan 0.0500 1.8581
## 20 14.5350 nan 0.0500 0.6874
## 40 6.1512 nan 0.0500 0.1254
## 60 4.0354 nan 0.0500 0.0327
## 80 3.4133 nan 0.0500 0.0098
## 100 3.1163 nan 0.0500 -0.0093
## 120 2.9128 nan 0.0500 -0.0379
## 140 2.7340 nan 0.0500 -0.0071
## 160 2.6080 nan 0.0500 -0.0191
## 180 2.5071 nan 0.0500 -0.0066
## 200 2.4102 nan 0.0500 -0.0176
## 220 2.3238 nan 0.0500 -0.0201
## 240 2.2269 nan 0.0500 -0.0106
## 260 2.1276 nan 0.0500 -0.0070
## 280 2.0660 nan 0.0500 -0.0171
## 300 1.9850 nan 0.0500 -0.0064
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.4497 nan 0.0500 5.2571
## 2 51.4948 nan 0.0500 4.3872
## 3 47.2237 nan 0.0500 4.2788
## 4 43.1012 nan 0.0500 3.9568
## 5 39.6110 nan 0.0500 3.7716
## 6 36.4250 nan 0.0500 3.1385
## 7 33.5711 nan 0.0500 2.8407
## 8 31.0495 nan 0.0500 2.4829
## 9 28.6009 nan 0.0500 2.5751
## 10 26.3251 nan 0.0500 2.1336
## 20 12.4240 nan 0.0500 0.8579
## 40 4.4064 nan 0.0500 0.0845
## 60 2.8496 nan 0.0500 0.0214
## 80 2.3113 nan 0.0500 -0.0207
## 100 1.9595 nan 0.0500 0.0034
## 120 1.7357 nan 0.0500 -0.0081
## 140 1.5718 nan 0.0500 -0.0148
## 160 1.4374 nan 0.0500 -0.0039
## 180 1.3285 nan 0.0500 -0.0044
## 200 1.2149 nan 0.0500 -0.0072
## 220 1.1142 nan 0.0500 -0.0084
## 240 1.0212 nan 0.0500 -0.0084
## 260 0.9448 nan 0.0500 -0.0079
## 280 0.8773 nan 0.0500 -0.0051
## 300 0.8178 nan 0.0500 -0.0071
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.1252 nan 0.0500 5.7414
## 2 51.4204 nan 0.0500 4.1735
## 3 46.8492 nan 0.0500 4.3520
## 4 42.9720 nan 0.0500 3.6947
## 5 39.4560 nan 0.0500 3.4321
## 6 36.0655 nan 0.0500 3.3346
## 7 33.0533 nan 0.0500 2.4446
## 8 30.4136 nan 0.0500 2.4410
## 9 27.9294 nan 0.0500 2.0733
## 10 25.8751 nan 0.0500 1.9461
## 20 12.3666 nan 0.0500 0.7241
## 40 4.5891 nan 0.0500 0.1474
## 60 3.0616 nan 0.0500 0.0053
## 80 2.5462 nan 0.0500 -0.0135
## 100 2.2825 nan 0.0500 0.0018
## 120 2.0888 nan 0.0500 -0.0110
## 140 1.9180 nan 0.0500 -0.0089
## 160 1.7699 nan 0.0500 -0.0045
## 180 1.6561 nan 0.0500 -0.0124
## 200 1.5505 nan 0.0500 -0.0150
## 220 1.4600 nan 0.0500 -0.0164
## 240 1.3829 nan 0.0500 -0.0084
## 260 1.3043 nan 0.0500 -0.0112
## 280 1.2221 nan 0.0500 -0.0104
## 300 1.1498 nan 0.0500 -0.0104
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.5323 nan 0.0500 4.4028
## 2 51.6696 nan 0.0500 4.9683
## 3 47.4301 nan 0.0500 4.4332
## 4 43.4831 nan 0.0500 3.5643
## 5 39.9808 nan 0.0500 3.6605
## 6 36.7691 nan 0.0500 2.8788
## 7 33.8249 nan 0.0500 2.6688
## 8 31.2318 nan 0.0500 2.5521
## 9 28.7939 nan 0.0500 2.2137
## 10 26.6234 nan 0.0500 2.2237
## 20 12.8953 nan 0.0500 0.9425
## 40 5.0654 nan 0.0500 0.1479
## 60 3.4858 nan 0.0500 -0.0025
## 80 2.9627 nan 0.0500 -0.0176
## 100 2.6740 nan 0.0500 -0.0081
## 120 2.4513 nan 0.0500 -0.0176
## 140 2.2664 nan 0.0500 -0.0130
## 160 2.1224 nan 0.0500 -0.0156
## 180 1.9930 nan 0.0500 -0.0065
## 200 1.8956 nan 0.0500 -0.0073
## 220 1.8014 nan 0.0500 -0.0104
## 240 1.7221 nan 0.0500 -0.0064
## 260 1.6321 nan 0.0500 -0.0195
## 280 1.5648 nan 0.0500 -0.0026
## 300 1.5015 nan 0.0500 -0.0089
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.9441 nan 0.1000 7.2420
## 2 47.8271 nan 0.1000 6.1404
## 3 42.9778 nan 0.1000 5.0376
## 4 38.4820 nan 0.1000 4.3274
## 5 34.5962 nan 0.1000 3.3048
## 6 31.2972 nan 0.1000 3.4166
## 7 28.1830 nan 0.1000 2.7391
## 8 26.0307 nan 0.1000 2.2424
## 9 23.8398 nan 0.1000 1.9182
## 10 21.7298 nan 0.1000 1.9186
## 20 10.8547 nan 0.1000 0.4527
## 40 5.1427 nan 0.1000 0.0812
## 60 3.8430 nan 0.1000 -0.0041
## 80 3.4858 nan 0.1000 -0.0395
## 100 3.2962 nan 0.1000 -0.0252
## 120 3.1812 nan 0.1000 -0.0162
## 140 3.1310 nan 0.1000 -0.0203
## 160 3.0434 nan 0.1000 -0.0263
## 180 2.9638 nan 0.1000 -0.0199
## 200 2.8911 nan 0.1000 -0.0234
## 220 2.8315 nan 0.1000 -0.0251
## 240 2.7713 nan 0.1000 -0.0103
## 260 2.7231 nan 0.1000 -0.0212
## 280 2.6790 nan 0.1000 -0.0173
## 300 2.6449 nan 0.1000 -0.0278
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.2811 nan 0.1000 7.7156
## 2 48.5306 nan 0.1000 5.1232
## 3 43.1711 nan 0.1000 5.3144
## 4 38.7685 nan 0.1000 4.2570
## 5 35.3232 nan 0.1000 3.6249
## 6 31.6729 nan 0.1000 3.3728
## 7 28.6366 nan 0.1000 2.9479
## 8 26.3530 nan 0.1000 2.1285
## 9 24.2800 nan 0.1000 2.1439
## 10 22.1598 nan 0.1000 1.8364
## 20 11.3253 nan 0.1000 0.7188
## 40 5.3606 nan 0.1000 0.0523
## 60 4.0782 nan 0.1000 0.0203
## 80 3.7381 nan 0.1000 -0.0153
## 100 3.5337 nan 0.1000 0.0049
## 120 3.3590 nan 0.1000 -0.0180
## 140 3.2575 nan 0.1000 -0.0073
## 160 3.1802 nan 0.1000 -0.0311
## 180 3.0985 nan 0.1000 -0.0099
## 200 3.0269 nan 0.1000 -0.0199
## 220 2.9687 nan 0.1000 -0.0187
## 240 2.9297 nan 0.1000 -0.0084
## 260 2.8581 nan 0.1000 -0.0240
## 280 2.8179 nan 0.1000 -0.0120
## 300 2.7729 nan 0.1000 -0.0103
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.4760 nan 0.1000 7.6332
## 2 47.5863 nan 0.1000 5.2700
## 3 42.2104 nan 0.1000 5.3414
## 4 37.7294 nan 0.1000 4.1433
## 5 33.6294 nan 0.1000 3.6448
## 6 30.1534 nan 0.1000 3.0533
## 7 27.2197 nan 0.1000 2.7926
## 8 24.8262 nan 0.1000 1.9462
## 9 22.6172 nan 0.1000 2.1359
## 10 20.7690 nan 0.1000 1.9686
## 20 10.7421 nan 0.1000 0.4988
## 40 5.2405 nan 0.1000 0.0935
## 60 4.0977 nan 0.1000 -0.0076
## 80 3.7719 nan 0.1000 0.0003
## 100 3.5611 nan 0.1000 -0.0232
## 120 3.4025 nan 0.1000 -0.0198
## 140 3.2895 nan 0.1000 -0.0189
## 160 3.2133 nan 0.1000 -0.0145
## 180 3.1242 nan 0.1000 -0.0129
## 200 3.0728 nan 0.1000 -0.0185
## 220 2.9869 nan 0.1000 -0.0152
## 240 2.9350 nan 0.1000 -0.0125
## 260 2.8926 nan 0.1000 -0.0142
## 280 2.8366 nan 0.1000 -0.0054
## 300 2.8009 nan 0.1000 -0.0111
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.2862 nan 0.1000 10.3712
## 2 43.4136 nan 0.1000 6.2938
## 3 36.8516 nan 0.1000 6.5241
## 4 31.3711 nan 0.1000 5.7309
## 5 27.3633 nan 0.1000 4.0482
## 6 23.4733 nan 0.1000 3.8651
## 7 20.2912 nan 0.1000 3.2508
## 8 17.7051 nan 0.1000 2.4428
## 9 15.4944 nan 0.1000 1.9867
## 10 13.5572 nan 0.1000 1.8592
## 20 5.5870 nan 0.1000 0.3227
## 40 2.9582 nan 0.1000 -0.0131
## 60 2.4322 nan 0.1000 0.0033
## 80 2.1164 nan 0.1000 -0.0086
## 100 1.8792 nan 0.1000 -0.0453
## 120 1.7086 nan 0.1000 -0.0026
## 140 1.5568 nan 0.1000 -0.0170
## 160 1.4502 nan 0.1000 -0.0178
## 180 1.3317 nan 0.1000 -0.0101
## 200 1.2255 nan 0.1000 -0.0256
## 220 1.1453 nan 0.1000 -0.0156
## 240 1.0606 nan 0.1000 -0.0133
## 260 0.9894 nan 0.1000 -0.0109
## 280 0.9172 nan 0.1000 -0.0093
## 300 0.8677 nan 0.1000 -0.0068
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.7152 nan 0.1000 9.1557
## 2 43.8506 nan 0.1000 6.9289
## 3 37.0778 nan 0.1000 5.8324
## 4 31.7620 nan 0.1000 5.4133
## 5 27.2661 nan 0.1000 3.8028
## 6 23.4308 nan 0.1000 3.3356
## 7 20.2442 nan 0.1000 2.9293
## 8 17.7938 nan 0.1000 2.2916
## 9 15.8277 nan 0.1000 2.0609
## 10 13.9468 nan 0.1000 1.6470
## 20 5.6296 nan 0.1000 0.3829
## 40 3.1728 nan 0.1000 -0.0289
## 60 2.6891 nan 0.1000 -0.0633
## 80 2.3955 nan 0.1000 -0.0499
## 100 2.2032 nan 0.1000 -0.0224
## 120 2.0237 nan 0.1000 -0.0349
## 140 1.8670 nan 0.1000 -0.0168
## 160 1.7399 nan 0.1000 -0.0255
## 180 1.6253 nan 0.1000 -0.0057
## 200 1.4989 nan 0.1000 -0.0107
## 220 1.3953 nan 0.1000 -0.0080
## 240 1.3051 nan 0.1000 -0.0089
## 260 1.2243 nan 0.1000 -0.0156
## 280 1.1630 nan 0.1000 -0.0087
## 300 1.1043 nan 0.1000 -0.0125
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.4981 nan 0.1000 8.8722
## 2 43.6963 nan 0.1000 6.9849
## 3 37.1869 nan 0.1000 6.6393
## 4 31.9387 nan 0.1000 5.5176
## 5 27.3425 nan 0.1000 4.1106
## 6 23.4002 nan 0.1000 3.9126
## 7 20.4652 nan 0.1000 2.9522
## 8 17.9750 nan 0.1000 2.4698
## 9 15.8197 nan 0.1000 1.8620
## 10 13.8155 nan 0.1000 1.6188
## 20 5.8215 nan 0.1000 0.3007
## 40 3.4309 nan 0.1000 -0.0030
## 60 2.9204 nan 0.1000 0.0017
## 80 2.5932 nan 0.1000 -0.0239
## 100 2.3570 nan 0.1000 -0.0199
## 120 2.1887 nan 0.1000 -0.0062
## 140 2.0648 nan 0.1000 -0.0162
## 160 1.9457 nan 0.1000 -0.0180
## 180 1.8223 nan 0.1000 -0.0215
## 200 1.7148 nan 0.1000 -0.0220
## 220 1.6322 nan 0.1000 -0.0170
## 240 1.5625 nan 0.1000 -0.0116
## 260 1.5047 nan 0.1000 -0.0212
## 280 1.4416 nan 0.1000 -0.0127
## 300 1.3683 nan 0.1000 -0.0134
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.1744 nan 0.1000 9.3356
## 2 42.8662 nan 0.1000 7.7694
## 3 35.9292 nan 0.1000 6.2969
## 4 30.1164 nan 0.1000 5.0818
## 5 25.7409 nan 0.1000 4.2125
## 6 21.7417 nan 0.1000 3.8264
## 7 18.6213 nan 0.1000 3.0381
## 8 15.6919 nan 0.1000 2.4127
## 9 13.3818 nan 0.1000 2.0423
## 10 11.6152 nan 0.1000 1.2527
## 20 4.3874 nan 0.1000 0.2272
## 40 2.3717 nan 0.1000 0.0035
## 60 1.8397 nan 0.1000 0.0016
## 80 1.5327 nan 0.1000 -0.0209
## 100 1.3159 nan 0.1000 -0.0375
## 120 1.1107 nan 0.1000 -0.0204
## 140 0.9601 nan 0.1000 -0.0145
## 160 0.8381 nan 0.1000 -0.0111
## 180 0.7415 nan 0.1000 -0.0050
## 200 0.6485 nan 0.1000 -0.0091
## 220 0.5703 nan 0.1000 -0.0078
## 240 0.5192 nan 0.1000 -0.0112
## 260 0.4702 nan 0.1000 -0.0083
## 280 0.4251 nan 0.1000 -0.0114
## 300 0.3761 nan 0.1000 -0.0072
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.4627 nan 0.1000 10.8696
## 2 43.1587 nan 0.1000 8.7122
## 3 35.9611 nan 0.1000 6.8755
## 4 30.4888 nan 0.1000 4.8808
## 5 25.9710 nan 0.1000 4.9787
## 6 22.2309 nan 0.1000 3.3146
## 7 19.1096 nan 0.1000 3.0082
## 8 16.5596 nan 0.1000 2.3615
## 9 14.2646 nan 0.1000 1.8845
## 10 12.4170 nan 0.1000 1.8401
## 20 4.5808 nan 0.1000 0.2301
## 40 2.6547 nan 0.1000 -0.0467
## 60 2.2096 nan 0.1000 -0.0087
## 80 1.8899 nan 0.1000 -0.0253
## 100 1.6260 nan 0.1000 -0.0434
## 120 1.4313 nan 0.1000 -0.0183
## 140 1.2741 nan 0.1000 -0.0183
## 160 1.1372 nan 0.1000 -0.0096
## 180 1.0120 nan 0.1000 -0.0131
## 200 0.9245 nan 0.1000 -0.0136
## 220 0.8249 nan 0.1000 -0.0091
## 240 0.7598 nan 0.1000 -0.0113
## 260 0.6955 nan 0.1000 -0.0096
## 280 0.6406 nan 0.1000 -0.0045
## 300 0.5900 nan 0.1000 -0.0158
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.5008 nan 0.1000 10.6072
## 2 43.2465 nan 0.1000 8.1726
## 3 35.9253 nan 0.1000 7.0885
## 4 30.6692 nan 0.1000 5.1548
## 5 25.9915 nan 0.1000 4.4196
## 6 22.3171 nan 0.1000 3.6442
## 7 19.2065 nan 0.1000 2.9365
## 8 16.4438 nan 0.1000 2.4022
## 9 14.6115 nan 0.1000 2.1859
## 10 12.7389 nan 0.1000 1.6453
## 20 5.0581 nan 0.1000 0.2565
## 40 3.0549 nan 0.1000 -0.0185
## 60 2.5366 nan 0.1000 -0.0198
## 80 2.1868 nan 0.1000 -0.0116
## 100 1.9244 nan 0.1000 -0.0173
## 120 1.7636 nan 0.1000 -0.0295
## 140 1.6170 nan 0.1000 -0.0245
## 160 1.4767 nan 0.1000 -0.0115
## 180 1.3540 nan 0.1000 -0.0082
## 200 1.2397 nan 0.1000 -0.0068
## 220 1.1560 nan 0.1000 -0.0339
## 240 1.0796 nan 0.1000 -0.0165
## 260 1.0065 nan 0.1000 -0.0076
## 280 0.9416 nan 0.1000 -0.0055
## 300 0.8828 nan 0.1000 -0.0092
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.2879 nan 0.0100 0.7918
## 2 61.5685 nan 0.0100 0.7657
## 3 60.7858 nan 0.0100 0.7480
## 4 60.0537 nan 0.0100 0.6681
## 5 59.2752 nan 0.0100 0.7176
## 6 58.5217 nan 0.0100 0.7559
## 7 57.7783 nan 0.0100 0.6767
## 8 57.0283 nan 0.0100 0.7019
## 9 56.3555 nan 0.0100 0.6650
## 10 55.6776 nan 0.0100 0.6584
## 20 49.7642 nan 0.0100 0.5350
## 40 40.1004 nan 0.0100 0.3794
## 60 32.7544 nan 0.0100 0.2946
## 80 27.3242 nan 0.0100 0.2446
## 100 23.0147 nan 0.0100 0.1844
## 120 19.5939 nan 0.0100 0.1319
## 140 16.9658 nan 0.0100 0.1027
## 160 14.8136 nan 0.0100 0.0811
## 180 13.1525 nan 0.0100 0.0689
## 200 11.6936 nan 0.0100 0.0564
## 220 10.5508 nan 0.0100 0.0321
## 240 9.5690 nan 0.0100 0.0397
## 260 8.7437 nan 0.0100 0.0307
## 280 8.0567 nan 0.0100 0.0194
## 300 7.4696 nan 0.0100 0.0115
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.2548 nan 0.0100 0.8061
## 2 61.4699 nan 0.0100 0.7915
## 3 60.7402 nan 0.0100 0.7577
## 4 59.8992 nan 0.0100 0.7334
## 5 59.1060 nan 0.0100 0.7529
## 6 58.3326 nan 0.0100 0.7964
## 7 57.6439 nan 0.0100 0.6765
## 8 56.8926 nan 0.0100 0.7111
## 9 56.2014 nan 0.0100 0.5897
## 10 55.4997 nan 0.0100 0.6717
## 20 49.5355 nan 0.0100 0.5851
## 40 40.0924 nan 0.0100 0.3333
## 60 32.7896 nan 0.0100 0.2942
## 80 27.3288 nan 0.0100 0.2141
## 100 23.0735 nan 0.0100 0.1567
## 120 19.6550 nan 0.0100 0.1192
## 140 16.8807 nan 0.0100 0.0819
## 160 14.7496 nan 0.0100 0.0874
## 180 13.0081 nan 0.0100 0.0522
## 200 11.6161 nan 0.0100 0.0438
## 220 10.4591 nan 0.0100 0.0441
## 240 9.4867 nan 0.0100 0.0511
## 260 8.6952 nan 0.0100 0.0252
## 280 7.9925 nan 0.0100 0.0242
## 300 7.3941 nan 0.0100 0.0219
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.2376 nan 0.0100 0.7897
## 2 61.4274 nan 0.0100 0.7131
## 3 60.6555 nan 0.0100 0.7738
## 4 59.9434 nan 0.0100 0.6974
## 5 59.3473 nan 0.0100 0.5464
## 6 58.6124 nan 0.0100 0.7491
## 7 57.8910 nan 0.0100 0.7126
## 8 57.1892 nan 0.0100 0.7504
## 9 56.4092 nan 0.0100 0.7313
## 10 55.7567 nan 0.0100 0.6811
## 20 49.6541 nan 0.0100 0.5336
## 40 40.1147 nan 0.0100 0.3611
## 60 32.7073 nan 0.0100 0.3111
## 80 27.1754 nan 0.0100 0.2129
## 100 22.8383 nan 0.0100 0.1725
## 120 19.5164 nan 0.0100 0.1147
## 140 16.8971 nan 0.0100 0.1019
## 160 14.7879 nan 0.0100 0.0870
## 180 13.0736 nan 0.0100 0.0699
## 200 11.6870 nan 0.0100 0.0612
## 220 10.5845 nan 0.0100 0.0392
## 240 9.6453 nan 0.0100 0.0359
## 260 8.8397 nan 0.0100 0.0334
## 280 8.1239 nan 0.0100 0.0272
## 300 7.5593 nan 0.0100 0.0132
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.9539 nan 0.0100 1.1668
## 2 60.9824 nan 0.0100 0.9683
## 3 59.9876 nan 0.0100 0.8909
## 4 59.0259 nan 0.0100 0.9880
## 5 58.0166 nan 0.0100 1.0308
## 6 57.0378 nan 0.0100 0.9104
## 7 56.0812 nan 0.0100 0.9204
## 8 55.1884 nan 0.0100 0.8286
## 9 54.3192 nan 0.0100 0.8596
## 10 53.4327 nan 0.0100 0.8219
## 20 45.3861 nan 0.0100 0.6761
## 40 33.2536 nan 0.0100 0.5033
## 60 24.8681 nan 0.0100 0.3638
## 80 18.9626 nan 0.0100 0.2326
## 100 14.7304 nan 0.0100 0.1699
## 120 11.7940 nan 0.0100 0.1113
## 140 9.6475 nan 0.0100 0.0868
## 160 7.9975 nan 0.0100 0.0668
## 180 6.7912 nan 0.0100 0.0385
## 200 5.8523 nan 0.0100 0.0371
## 220 5.1535 nan 0.0100 0.0173
## 240 4.6072 nan 0.0100 0.0190
## 260 4.1991 nan 0.0100 0.0108
## 280 3.8961 nan 0.0100 0.0095
## 300 3.6450 nan 0.0100 0.0024
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.0046 nan 0.0100 1.0918
## 2 61.0094 nan 0.0100 0.9332
## 3 59.9559 nan 0.0100 0.9915
## 4 58.9444 nan 0.0100 1.0314
## 5 57.9191 nan 0.0100 1.0016
## 6 56.9410 nan 0.0100 0.9067
## 7 55.9738 nan 0.0100 0.9345
## 8 55.0426 nan 0.0100 0.8482
## 9 54.1755 nan 0.0100 0.8576
## 10 53.2549 nan 0.0100 0.8931
## 20 45.3461 nan 0.0100 0.6892
## 40 33.2212 nan 0.0100 0.4885
## 60 24.7630 nan 0.0100 0.3688
## 80 18.8274 nan 0.0100 0.2268
## 100 14.6583 nan 0.0100 0.1837
## 120 11.7113 nan 0.0100 0.1153
## 140 9.5935 nan 0.0100 0.0802
## 160 7.9960 nan 0.0100 0.0660
## 180 6.8114 nan 0.0100 0.0391
## 200 5.9353 nan 0.0100 0.0322
## 220 5.2488 nan 0.0100 0.0202
## 240 4.7346 nan 0.0100 0.0123
## 260 4.3508 nan 0.0100 0.0105
## 280 4.0461 nan 0.0100 0.0071
## 300 3.8115 nan 0.0100 0.0040
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.0001 nan 0.0100 1.0740
## 2 60.9038 nan 0.0100 1.1083
## 3 59.9266 nan 0.0100 0.9264
## 4 59.0104 nan 0.0100 0.9228
## 5 58.0411 nan 0.0100 0.9830
## 6 57.0756 nan 0.0100 1.0119
## 7 56.0865 nan 0.0100 0.8535
## 8 55.1521 nan 0.0100 1.0303
## 9 54.2604 nan 0.0100 0.8890
## 10 53.3742 nan 0.0100 0.8876
## 20 45.3899 nan 0.0100 0.7436
## 40 33.4799 nan 0.0100 0.5031
## 60 24.9248 nan 0.0100 0.3397
## 80 18.9729 nan 0.0100 0.2201
## 100 14.8811 nan 0.0100 0.1648
## 120 11.9465 nan 0.0100 0.1118
## 140 9.8061 nan 0.0100 0.0858
## 160 8.1776 nan 0.0100 0.0678
## 180 6.9984 nan 0.0100 0.0412
## 200 6.1040 nan 0.0100 0.0229
## 220 5.4288 nan 0.0100 0.0285
## 240 4.9209 nan 0.0100 0.0114
## 260 4.5227 nan 0.0100 0.0147
## 280 4.2334 nan 0.0100 0.0076
## 300 4.0007 nan 0.0100 0.0035
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.9837 nan 0.0100 1.2660
## 2 60.8948 nan 0.0100 1.0841
## 3 59.8178 nan 0.0100 1.0196
## 4 58.7476 nan 0.0100 1.0885
## 5 57.7419 nan 0.0100 0.9799
## 6 56.6968 nan 0.0100 0.9575
## 7 55.6822 nan 0.0100 0.9727
## 8 54.7111 nan 0.0100 0.8663
## 9 53.7506 nan 0.0100 1.0013
## 10 52.8013 nan 0.0100 0.8626
## 20 44.3519 nan 0.0100 0.7802
## 40 31.7695 nan 0.0100 0.4407
## 60 23.1116 nan 0.0100 0.3589
## 80 17.1328 nan 0.0100 0.2116
## 100 12.9814 nan 0.0100 0.1636
## 120 10.0087 nan 0.0100 0.1206
## 140 7.9274 nan 0.0100 0.0700
## 160 6.4625 nan 0.0100 0.0539
## 180 5.4034 nan 0.0100 0.0401
## 200 4.6191 nan 0.0100 0.0308
## 220 4.0379 nan 0.0100 0.0180
## 240 3.6145 nan 0.0100 0.0107
## 260 3.2971 nan 0.0100 0.0077
## 280 3.0615 nan 0.0100 0.0058
## 300 2.8623 nan 0.0100 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.9774 nan 0.0100 1.0771
## 2 60.8538 nan 0.0100 0.9865
## 3 59.7732 nan 0.0100 1.1052
## 4 58.7242 nan 0.0100 0.9586
## 5 57.7486 nan 0.0100 0.9731
## 6 56.7873 nan 0.0100 0.9770
## 7 55.8306 nan 0.0100 0.9614
## 8 54.8646 nan 0.0100 0.9977
## 9 53.9152 nan 0.0100 1.0610
## 10 52.9340 nan 0.0100 0.8258
## 20 44.5457 nan 0.0100 0.7060
## 40 32.0156 nan 0.0100 0.4970
## 60 23.2883 nan 0.0100 0.3717
## 80 17.2602 nan 0.0100 0.2312
## 100 12.9955 nan 0.0100 0.1558
## 120 10.0678 nan 0.0100 0.0866
## 140 8.0146 nan 0.0100 0.0738
## 160 6.5738 nan 0.0100 0.0539
## 180 5.5549 nan 0.0100 0.0431
## 200 4.7982 nan 0.0100 0.0277
## 220 4.2543 nan 0.0100 0.0086
## 240 3.8395 nan 0.0100 0.0132
## 260 3.5265 nan 0.0100 0.0030
## 280 3.2940 nan 0.0100 0.0060
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.9154 nan 0.0100 1.1238
## 2 60.8149 nan 0.0100 1.1661
## 3 59.7617 nan 0.0100 1.1066
## 4 58.7571 nan 0.0100 0.9320
## 5 57.7178 nan 0.0100 0.9879
## 6 56.7076 nan 0.0100 1.0182
## 7 55.7295 nan 0.0100 0.8906
## 8 54.7830 nan 0.0100 0.9281
## 9 53.7979 nan 0.0100 0.9521
## 10 52.8848 nan 0.0100 0.9026
## 20 44.3568 nan 0.0100 0.7371
## 40 31.8351 nan 0.0100 0.5580
## 60 23.2002 nan 0.0100 0.2975
## 80 17.3519 nan 0.0100 0.2460
## 100 13.2818 nan 0.0100 0.1677
## 120 10.3823 nan 0.0100 0.1282
## 140 8.3679 nan 0.0100 0.0746
## 160 6.9204 nan 0.0100 0.0556
## 180 5.8913 nan 0.0100 0.0357
## 200 5.1662 nan 0.0100 0.0266
## 220 4.6272 nan 0.0100 0.0134
## 240 4.2216 nan 0.0100 0.0055
## 260 3.9407 nan 0.0100 0.0066
## 280 3.7110 nan 0.0100 0.0042
## 300 3.5175 nan 0.0100 0.0014
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.3956 nan 0.0500 3.8244
## 2 55.6653 nan 0.0500 3.5007
## 3 52.5359 nan 0.0500 3.4368
## 4 49.7533 nan 0.0500 2.9200
## 5 46.9020 nan 0.0500 2.5685
## 6 44.4170 nan 0.0500 2.2151
## 7 41.7859 nan 0.0500 2.7660
## 8 39.8121 nan 0.0500 1.8870
## 9 37.6411 nan 0.0500 1.9385
## 10 35.6523 nan 0.0500 1.9580
## 20 22.8008 nan 0.0500 0.9617
## 40 11.8900 nan 0.0500 0.3024
## 60 7.5376 nan 0.0500 0.1444
## 80 5.5703 nan 0.0500 0.0462
## 100 4.5750 nan 0.0500 0.0356
## 120 4.0844 nan 0.0500 -0.0111
## 140 3.8096 nan 0.0500 -0.0011
## 160 3.6646 nan 0.0500 -0.0071
## 180 3.5481 nan 0.0500 -0.0235
## 200 3.4625 nan 0.0500 -0.0069
## 220 3.3860 nan 0.0500 -0.0044
## 240 3.3084 nan 0.0500 -0.0041
## 260 3.2543 nan 0.0500 -0.0039
## 280 3.2087 nan 0.0500 -0.0103
## 300 3.1608 nan 0.0500 -0.0035
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.3533 nan 0.0500 4.0646
## 2 55.7381 nan 0.0500 3.6016
## 3 52.2986 nan 0.0500 2.9537
## 4 49.0737 nan 0.0500 2.8889
## 5 46.4285 nan 0.0500 2.6391
## 6 43.8298 nan 0.0500 2.2559
## 7 41.4434 nan 0.0500 2.0393
## 8 39.3361 nan 0.0500 1.8964
## 9 37.4128 nan 0.0500 1.8635
## 10 35.5320 nan 0.0500 1.5653
## 20 22.3762 nan 0.0500 0.6353
## 40 11.4130 nan 0.0500 0.2955
## 60 7.3123 nan 0.0500 0.1036
## 80 5.5370 nan 0.0500 0.0393
## 100 4.5894 nan 0.0500 0.0257
## 120 4.1180 nan 0.0500 -0.0089
## 140 3.8594 nan 0.0500 -0.0062
## 160 3.7206 nan 0.0500 -0.0059
## 180 3.6166 nan 0.0500 -0.0042
## 200 3.5120 nan 0.0500 -0.0006
## 220 3.4236 nan 0.0500 -0.0028
## 240 3.3525 nan 0.0500 -0.0119
## 260 3.2847 nan 0.0500 -0.0046
## 280 3.2129 nan 0.0500 -0.0060
## 300 3.1649 nan 0.0500 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.1588 nan 0.0500 3.8371
## 2 55.3994 nan 0.0500 3.4684
## 3 52.2227 nan 0.0500 2.8593
## 4 49.1109 nan 0.0500 2.8627
## 5 46.3233 nan 0.0500 2.5798
## 6 44.1209 nan 0.0500 2.1129
## 7 41.6351 nan 0.0500 2.3346
## 8 39.7039 nan 0.0500 2.0924
## 9 37.3696 nan 0.0500 1.8478
## 10 35.4331 nan 0.0500 1.4631
## 20 22.3967 nan 0.0500 0.9002
## 40 11.4412 nan 0.0500 0.3380
## 60 7.4127 nan 0.0500 0.1385
## 80 5.6496 nan 0.0500 0.0358
## 100 4.8217 nan 0.0500 0.0150
## 120 4.4031 nan 0.0500 0.0026
## 140 4.1762 nan 0.0500 0.0078
## 160 4.0230 nan 0.0500 0.0039
## 180 3.9136 nan 0.0500 -0.0075
## 200 3.7983 nan 0.0500 -0.0005
## 220 3.7045 nan 0.0500 -0.0007
## 240 3.6205 nan 0.0500 0.0013
## 260 3.5399 nan 0.0500 -0.0111
## 280 3.4428 nan 0.0500 -0.0072
## 300 3.3816 nan 0.0500 0.0008
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.8096 nan 0.0500 4.5339
## 2 53.2912 nan 0.0500 4.8666
## 3 49.0263 nan 0.0500 4.0866
## 4 45.0731 nan 0.0500 3.6521
## 5 41.7066 nan 0.0500 3.4011
## 6 38.5323 nan 0.0500 2.9258
## 7 35.5658 nan 0.0500 2.9457
## 8 32.8483 nan 0.0500 2.6071
## 9 30.4466 nan 0.0500 2.4743
## 10 28.1842 nan 0.0500 2.3287
## 20 14.4072 nan 0.0500 0.8618
## 40 5.6984 nan 0.0500 0.1395
## 60 3.6580 nan 0.0500 0.0217
## 80 2.9315 nan 0.0500 0.0156
## 100 2.5774 nan 0.0500 0.0003
## 120 2.3260 nan 0.0500 -0.0031
## 140 2.1214 nan 0.0500 -0.0151
## 160 1.9488 nan 0.0500 -0.0079
## 180 1.8195 nan 0.0500 -0.0107
## 200 1.7380 nan 0.0500 -0.0136
## 220 1.6520 nan 0.0500 -0.0037
## 240 1.5563 nan 0.0500 -0.0117
## 260 1.4752 nan 0.0500 -0.0059
## 280 1.3987 nan 0.0500 -0.0109
## 300 1.3375 nan 0.0500 -0.0120
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.0403 nan 0.0500 4.9046
## 2 53.6307 nan 0.0500 4.7823
## 3 49.2229 nan 0.0500 4.6157
## 4 45.3146 nan 0.0500 3.9865
## 5 41.5877 nan 0.0500 3.4740
## 6 38.4794 nan 0.0500 3.3244
## 7 35.5854 nan 0.0500 2.7736
## 8 32.7445 nan 0.0500 2.3901
## 9 30.5538 nan 0.0500 2.2682
## 10 28.3767 nan 0.0500 2.3250
## 20 14.6102 nan 0.0500 0.8099
## 40 5.9244 nan 0.0500 0.1538
## 60 3.8559 nan 0.0500 0.0277
## 80 3.1458 nan 0.0500 -0.0006
## 100 2.7893 nan 0.0500 0.0020
## 120 2.5468 nan 0.0500 -0.0062
## 140 2.3860 nan 0.0500 -0.0029
## 160 2.2235 nan 0.0500 -0.0117
## 180 2.0948 nan 0.0500 -0.0013
## 200 1.9998 nan 0.0500 -0.0088
## 220 1.9197 nan 0.0500 -0.0115
## 240 1.8368 nan 0.0500 -0.0134
## 260 1.7651 nan 0.0500 -0.0107
## 280 1.7017 nan 0.0500 -0.0059
## 300 1.6435 nan 0.0500 -0.0104
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.7374 nan 0.0500 6.0841
## 2 53.0715 nan 0.0500 4.4158
## 3 48.9888 nan 0.0500 4.3379
## 4 45.1731 nan 0.0500 3.8862
## 5 41.6098 nan 0.0500 3.7575
## 6 38.2443 nan 0.0500 2.9238
## 7 35.5846 nan 0.0500 2.6468
## 8 32.8897 nan 0.0500 2.6699
## 9 30.4454 nan 0.0500 2.4746
## 10 28.2602 nan 0.0500 2.1774
## 20 14.7010 nan 0.0500 0.8192
## 40 6.0886 nan 0.0500 0.1375
## 60 4.0019 nan 0.0500 0.0443
## 80 3.3635 nan 0.0500 -0.0001
## 100 3.0289 nan 0.0500 -0.0185
## 120 2.7856 nan 0.0500 -0.0129
## 140 2.5755 nan 0.0500 -0.0099
## 160 2.4370 nan 0.0500 -0.0085
## 180 2.3030 nan 0.0500 -0.0055
## 200 2.1950 nan 0.0500 -0.0146
## 220 2.0819 nan 0.0500 -0.0065
## 240 1.9920 nan 0.0500 -0.0106
## 260 1.9206 nan 0.0500 -0.0125
## 280 1.8515 nan 0.0500 -0.0042
## 300 1.7872 nan 0.0500 -0.0075
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.3901 nan 0.0500 5.9261
## 2 52.7396 nan 0.0500 4.9300
## 3 48.0968 nan 0.0500 4.5091
## 4 44.0206 nan 0.0500 3.8374
## 5 40.3544 nan 0.0500 3.9691
## 6 36.8693 nan 0.0500 3.4354
## 7 33.7800 nan 0.0500 3.0243
## 8 30.9552 nan 0.0500 2.6098
## 9 28.5795 nan 0.0500 2.3263
## 10 26.3316 nan 0.0500 2.1134
## 20 12.5268 nan 0.0500 0.8300
## 40 4.5924 nan 0.0500 0.1748
## 60 2.9367 nan 0.0500 -0.0016
## 80 2.3302 nan 0.0500 -0.0138
## 100 2.0168 nan 0.0500 -0.0226
## 120 1.7391 nan 0.0500 -0.0108
## 140 1.5756 nan 0.0500 -0.0085
## 160 1.4201 nan 0.0500 -0.0081
## 180 1.2799 nan 0.0500 -0.0068
## 200 1.1660 nan 0.0500 -0.0067
## 220 1.0653 nan 0.0500 -0.0126
## 240 0.9907 nan 0.0500 -0.0103
## 260 0.9093 nan 0.0500 -0.0069
## 280 0.8416 nan 0.0500 -0.0059
## 300 0.7864 nan 0.0500 -0.0095
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.8166 nan 0.0500 5.4690
## 2 52.9766 nan 0.0500 4.7273
## 3 48.5490 nan 0.0500 3.9240
## 4 44.3757 nan 0.0500 3.8684
## 5 40.6733 nan 0.0500 3.3667
## 6 37.1948 nan 0.0500 3.4309
## 7 34.1868 nan 0.0500 2.8596
## 8 31.4257 nan 0.0500 2.5529
## 9 28.8096 nan 0.0500 2.2876
## 10 26.4545 nan 0.0500 2.1163
## 20 12.6418 nan 0.0500 0.8235
## 40 4.6435 nan 0.0500 0.1253
## 60 3.0974 nan 0.0500 0.0176
## 80 2.5902 nan 0.0500 -0.0099
## 100 2.2615 nan 0.0500 0.0047
## 120 2.0305 nan 0.0500 -0.0127
## 140 1.8496 nan 0.0500 -0.0063
## 160 1.7091 nan 0.0500 -0.0193
## 180 1.5882 nan 0.0500 -0.0121
## 200 1.4789 nan 0.0500 -0.0049
## 220 1.3840 nan 0.0500 -0.0060
## 240 1.2883 nan 0.0500 -0.0091
## 260 1.2151 nan 0.0500 -0.0124
## 280 1.1527 nan 0.0500 -0.0075
## 300 1.0926 nan 0.0500 -0.0072
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.8439 nan 0.0500 5.4330
## 2 53.0236 nan 0.0500 5.1841
## 3 48.7113 nan 0.0500 4.0321
## 4 44.5724 nan 0.0500 3.9382
## 5 40.8458 nan 0.0500 3.9470
## 6 37.5173 nan 0.0500 2.8976
## 7 34.3400 nan 0.0500 3.1000
## 8 31.5673 nan 0.0500 2.5679
## 9 29.1831 nan 0.0500 2.3236
## 10 26.8846 nan 0.0500 2.1696
## 20 13.1125 nan 0.0500 0.8076
## 40 5.1167 nan 0.0500 0.1116
## 60 3.5455 nan 0.0500 0.0092
## 80 2.9536 nan 0.0500 -0.0039
## 100 2.6051 nan 0.0500 -0.0036
## 120 2.3859 nan 0.0500 -0.0082
## 140 2.2062 nan 0.0500 -0.0117
## 160 2.0454 nan 0.0500 -0.0075
## 180 1.9187 nan 0.0500 -0.0103
## 200 1.8172 nan 0.0500 -0.0137
## 220 1.7101 nan 0.0500 -0.0039
## 240 1.6247 nan 0.0500 -0.0143
## 260 1.5270 nan 0.0500 -0.0049
## 280 1.4606 nan 0.0500 -0.0197
## 300 1.3837 nan 0.0500 -0.0090
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.1438 nan 0.1000 7.6410
## 2 49.6843 nan 0.1000 6.0802
## 3 43.7805 nan 0.1000 5.6168
## 4 39.0668 nan 0.1000 3.9554
## 5 35.4015 nan 0.1000 3.2502
## 6 31.6470 nan 0.1000 3.5191
## 7 28.8782 nan 0.1000 2.7394
## 8 26.1881 nan 0.1000 2.3165
## 9 23.8081 nan 0.1000 1.9816
## 10 21.5368 nan 0.1000 2.2400
## 20 11.1150 nan 0.1000 0.4144
## 40 5.4556 nan 0.1000 0.0768
## 60 4.1872 nan 0.1000 0.0067
## 80 3.8071 nan 0.1000 -0.0298
## 100 3.5672 nan 0.1000 -0.0086
## 120 3.4321 nan 0.1000 -0.0254
## 140 3.2762 nan 0.1000 -0.0119
## 160 3.1609 nan 0.1000 -0.0123
## 180 3.0841 nan 0.1000 -0.0098
## 200 3.0077 nan 0.1000 -0.0052
## 220 2.9371 nan 0.1000 -0.0359
## 240 2.8913 nan 0.1000 -0.0215
## 260 2.8233 nan 0.1000 -0.0148
## 280 2.7803 nan 0.1000 -0.0066
## 300 2.7299 nan 0.1000 -0.0101
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.8849 nan 0.1000 7.5552
## 2 49.8578 nan 0.1000 5.6390
## 3 44.5300 nan 0.1000 5.7536
## 4 40.2391 nan 0.1000 4.3531
## 5 35.9313 nan 0.1000 3.9622
## 6 32.4866 nan 0.1000 3.3353
## 7 29.1851 nan 0.1000 2.8554
## 8 26.4746 nan 0.1000 2.5684
## 9 24.0476 nan 0.1000 2.1929
## 10 22.2728 nan 0.1000 1.4910
## 20 11.0843 nan 0.1000 0.4982
## 40 5.4835 nan 0.1000 0.0019
## 60 4.2099 nan 0.1000 0.0249
## 80 3.8625 nan 0.1000 -0.0256
## 100 3.6130 nan 0.1000 -0.0086
## 120 3.4371 nan 0.1000 -0.0130
## 140 3.2897 nan 0.1000 -0.0045
## 160 3.1796 nan 0.1000 -0.0057
## 180 3.1010 nan 0.1000 -0.0033
## 200 3.0279 nan 0.1000 -0.0114
## 220 2.9605 nan 0.1000 -0.0274
## 240 2.8913 nan 0.1000 -0.0037
## 260 2.8233 nan 0.1000 -0.0040
## 280 2.7636 nan 0.1000 -0.0080
## 300 2.7211 nan 0.1000 -0.0071
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.9434 nan 0.1000 7.1297
## 2 49.3077 nan 0.1000 5.8306
## 3 43.7682 nan 0.1000 5.3651
## 4 39.4861 nan 0.1000 4.3868
## 5 35.4206 nan 0.1000 4.1375
## 6 31.8842 nan 0.1000 3.0288
## 7 28.9768 nan 0.1000 2.6172
## 8 26.6642 nan 0.1000 2.3271
## 9 24.1922 nan 0.1000 2.3758
## 10 22.1891 nan 0.1000 1.9361
## 20 11.4506 nan 0.1000 0.5568
## 40 5.6318 nan 0.1000 0.1027
## 60 4.4680 nan 0.1000 0.0143
## 80 4.0773 nan 0.1000 -0.0093
## 100 3.8534 nan 0.1000 -0.0392
## 120 3.6556 nan 0.1000 -0.0076
## 140 3.5054 nan 0.1000 -0.0039
## 160 3.3528 nan 0.1000 0.0001
## 180 3.2504 nan 0.1000 -0.0084
## 200 3.1698 nan 0.1000 -0.0244
## 220 3.0757 nan 0.1000 -0.0038
## 240 3.0177 nan 0.1000 -0.0069
## 260 2.9442 nan 0.1000 -0.0103
## 280 2.8672 nan 0.1000 -0.0272
## 300 2.7861 nan 0.1000 -0.0007
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.7412 nan 0.1000 8.9841
## 2 44.3088 nan 0.1000 7.7404
## 3 37.1985 nan 0.1000 6.9160
## 4 31.9109 nan 0.1000 5.3672
## 5 27.2800 nan 0.1000 4.0731
## 6 23.2697 nan 0.1000 3.5761
## 7 20.2830 nan 0.1000 3.1753
## 8 17.8072 nan 0.1000 2.3982
## 9 15.6642 nan 0.1000 1.8860
## 10 13.7190 nan 0.1000 1.9197
## 20 5.5605 nan 0.1000 0.3615
## 40 3.1109 nan 0.1000 0.0152
## 60 2.5465 nan 0.1000 -0.0117
## 80 2.1519 nan 0.1000 -0.0449
## 100 1.8751 nan 0.1000 -0.0148
## 120 1.7096 nan 0.1000 -0.0138
## 140 1.5726 nan 0.1000 -0.0331
## 160 1.4501 nan 0.1000 -0.0174
## 180 1.3180 nan 0.1000 -0.0102
## 200 1.1941 nan 0.1000 -0.0185
## 220 1.1013 nan 0.1000 -0.0145
## 240 1.0137 nan 0.1000 -0.0221
## 260 0.9558 nan 0.1000 -0.0062
## 280 0.8985 nan 0.1000 -0.0130
## 300 0.8435 nan 0.1000 -0.0068
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.3344 nan 0.1000 9.9743
## 2 45.0409 nan 0.1000 8.3071
## 3 38.1850 nan 0.1000 6.7013
## 4 32.6666 nan 0.1000 5.0264
## 5 27.8562 nan 0.1000 4.4839
## 6 24.1889 nan 0.1000 3.7144
## 7 20.9650 nan 0.1000 3.1286
## 8 18.5104 nan 0.1000 2.4828
## 9 16.2899 nan 0.1000 2.2134
## 10 14.4691 nan 0.1000 1.5834
## 20 5.8636 nan 0.1000 0.3076
## 40 3.1876 nan 0.1000 -0.0456
## 60 2.6828 nan 0.1000 -0.0121
## 80 2.2741 nan 0.1000 -0.0360
## 100 2.0673 nan 0.1000 -0.0053
## 120 1.8775 nan 0.1000 -0.0226
## 140 1.7333 nan 0.1000 -0.0471
## 160 1.6139 nan 0.1000 -0.0163
## 180 1.5099 nan 0.1000 -0.0066
## 200 1.4060 nan 0.1000 -0.0238
## 220 1.3298 nan 0.1000 -0.0185
## 240 1.2528 nan 0.1000 -0.0123
## 260 1.1764 nan 0.1000 -0.0145
## 280 1.1199 nan 0.1000 -0.0250
## 300 1.0599 nan 0.1000 -0.0105
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.0915 nan 0.1000 10.5705
## 2 45.0014 nan 0.1000 8.4517
## 3 37.9915 nan 0.1000 6.7091
## 4 32.4900 nan 0.1000 4.9277
## 5 27.9204 nan 0.1000 4.3298
## 6 23.9782 nan 0.1000 3.6528
## 7 20.8991 nan 0.1000 2.9252
## 8 18.2168 nan 0.1000 2.6738
## 9 15.9633 nan 0.1000 2.0135
## 10 14.1448 nan 0.1000 1.7032
## 20 5.9339 nan 0.1000 0.3635
## 40 3.4261 nan 0.1000 0.0246
## 60 2.8715 nan 0.1000 -0.0046
## 80 2.4889 nan 0.1000 -0.0150
## 100 2.2028 nan 0.1000 -0.0221
## 120 2.0199 nan 0.1000 -0.0183
## 140 1.8795 nan 0.1000 -0.0098
## 160 1.7380 nan 0.1000 -0.0168
## 180 1.6384 nan 0.1000 -0.0176
## 200 1.5427 nan 0.1000 -0.0064
## 220 1.4781 nan 0.1000 -0.0076
## 240 1.3925 nan 0.1000 -0.0115
## 260 1.3170 nan 0.1000 -0.0182
## 280 1.2523 nan 0.1000 -0.0174
## 300 1.2009 nan 0.1000 -0.0113
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.8281 nan 0.1000 9.3216
## 2 44.3513 nan 0.1000 7.7461
## 3 36.9729 nan 0.1000 6.8963
## 4 31.1202 nan 0.1000 5.2217
## 5 26.5263 nan 0.1000 4.6090
## 6 22.4745 nan 0.1000 3.6961
## 7 19.4208 nan 0.1000 3.2717
## 8 16.6406 nan 0.1000 2.5089
## 9 14.3394 nan 0.1000 2.0172
## 10 12.4630 nan 0.1000 1.2673
## 20 4.4724 nan 0.1000 0.2713
## 40 2.3520 nan 0.1000 0.0099
## 60 1.7759 nan 0.1000 -0.0302
## 80 1.4284 nan 0.1000 -0.0179
## 100 1.2134 nan 0.1000 -0.0193
## 120 1.0223 nan 0.1000 -0.0267
## 140 0.8623 nan 0.1000 -0.0044
## 160 0.7468 nan 0.1000 -0.0131
## 180 0.6454 nan 0.1000 -0.0061
## 200 0.5733 nan 0.1000 -0.0134
## 220 0.5096 nan 0.1000 -0.0125
## 240 0.4575 nan 0.1000 -0.0118
## 260 0.4090 nan 0.1000 -0.0093
## 280 0.3661 nan 0.1000 -0.0099
## 300 0.3285 nan 0.1000 -0.0085
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.9475 nan 0.1000 9.9414
## 2 44.3752 nan 0.1000 7.5275
## 3 37.1191 nan 0.1000 7.2150
## 4 31.2794 nan 0.1000 5.8318
## 5 26.3339 nan 0.1000 4.9722
## 6 22.3651 nan 0.1000 3.9869
## 7 19.0259 nan 0.1000 3.2341
## 8 16.5154 nan 0.1000 2.3179
## 9 14.4399 nan 0.1000 2.2921
## 10 12.6663 nan 0.1000 1.6430
## 20 4.6739 nan 0.1000 0.2106
## 40 2.6628 nan 0.1000 -0.0120
## 60 2.1095 nan 0.1000 -0.0118
## 80 1.8062 nan 0.1000 -0.0316
## 100 1.5762 nan 0.1000 -0.0224
## 120 1.3809 nan 0.1000 -0.0215
## 140 1.2163 nan 0.1000 -0.0200
## 160 1.0979 nan 0.1000 -0.0126
## 180 0.9896 nan 0.1000 -0.0176
## 200 0.8953 nan 0.1000 -0.0109
## 220 0.8216 nan 0.1000 -0.0189
## 240 0.7489 nan 0.1000 -0.0143
## 260 0.6933 nan 0.1000 -0.0114
## 280 0.6449 nan 0.1000 -0.0115
## 300 0.5809 nan 0.1000 -0.0081
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.2363 nan 0.1000 11.4975
## 2 43.7069 nan 0.1000 8.8512
## 3 36.6773 nan 0.1000 6.5151
## 4 30.7827 nan 0.1000 5.4660
## 5 26.2092 nan 0.1000 4.3566
## 6 22.4510 nan 0.1000 3.9423
## 7 19.2072 nan 0.1000 3.1462
## 8 16.8900 nan 0.1000 2.1972
## 9 14.5968 nan 0.1000 2.2840
## 10 12.9082 nan 0.1000 1.7076
## 20 5.0341 nan 0.1000 0.2392
## 40 3.0711 nan 0.1000 0.0147
## 60 2.5345 nan 0.1000 -0.0565
## 80 2.2039 nan 0.1000 -0.0094
## 100 1.9339 nan 0.1000 -0.0273
## 120 1.7305 nan 0.1000 -0.0242
## 140 1.5635 nan 0.1000 -0.0267
## 160 1.4299 nan 0.1000 -0.0267
## 180 1.3241 nan 0.1000 -0.0262
## 200 1.2199 nan 0.1000 -0.0199
## 220 1.1367 nan 0.1000 -0.0131
## 240 1.0470 nan 0.1000 -0.0157
## 260 0.9730 nan 0.1000 -0.0071
## 280 0.9186 nan 0.1000 -0.0094
## 300 0.8576 nan 0.1000 -0.0109
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.4123 nan 0.0100 0.7552
## 2 60.6847 nan 0.0100 0.7746
## 3 59.9616 nan 0.0100 0.7510
## 4 59.2769 nan 0.0100 0.7578
## 5 58.5815 nan 0.0100 0.7104
## 6 57.8613 nan 0.0100 0.7247
## 7 57.1910 nan 0.0100 0.7041
## 8 56.5057 nan 0.0100 0.6832
## 9 55.8467 nan 0.0100 0.6842
## 10 55.1497 nan 0.0100 0.6449
## 20 49.2898 nan 0.0100 0.4414
## 40 39.7083 nan 0.0100 0.4003
## 60 32.4471 nan 0.0100 0.2922
## 80 26.9294 nan 0.0100 0.2139
## 100 22.7901 nan 0.0100 0.1500
## 120 19.4928 nan 0.0100 0.1305
## 140 16.8889 nan 0.0100 0.1103
## 160 14.8397 nan 0.0100 0.0889
## 180 13.0977 nan 0.0100 0.0621
## 200 11.6923 nan 0.0100 0.0463
## 220 10.5519 nan 0.0100 0.0455
## 240 9.5875 nan 0.0100 0.0347
## 260 8.7787 nan 0.0100 0.0288
## 280 8.0850 nan 0.0100 0.0200
## 300 7.4825 nan 0.0100 0.0238
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.3597 nan 0.0100 0.7736
## 2 60.5635 nan 0.0100 0.7617
## 3 59.7441 nan 0.0100 0.7398
## 4 58.9336 nan 0.0100 0.7469
## 5 58.2203 nan 0.0100 0.7285
## 6 57.4655 nan 0.0100 0.7159
## 7 56.6781 nan 0.0100 0.6548
## 8 56.0807 nan 0.0100 0.6583
## 9 55.4060 nan 0.0100 0.6756
## 10 54.7378 nan 0.0100 0.6507
## 20 48.7677 nan 0.0100 0.5336
## 40 39.2732 nan 0.0100 0.3985
## 60 32.2951 nan 0.0100 0.3395
## 80 26.8741 nan 0.0100 0.2305
## 100 22.6560 nan 0.0100 0.1701
## 120 19.3996 nan 0.0100 0.1340
## 140 16.8076 nan 0.0100 0.1056
## 160 14.7149 nan 0.0100 0.0792
## 180 13.0342 nan 0.0100 0.0496
## 200 11.6096 nan 0.0100 0.0485
## 220 10.4521 nan 0.0100 0.0438
## 240 9.4651 nan 0.0100 0.0211
## 260 8.6537 nan 0.0100 0.0371
## 280 7.9659 nan 0.0100 0.0266
## 300 7.3938 nan 0.0100 0.0216
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.2880 nan 0.0100 0.8177
## 2 60.5290 nan 0.0100 0.7330
## 3 59.8133 nan 0.0100 0.7353
## 4 59.0411 nan 0.0100 0.7028
## 5 58.3450 nan 0.0100 0.6948
## 6 57.6646 nan 0.0100 0.6865
## 7 56.9669 nan 0.0100 0.7072
## 8 56.3224 nan 0.0100 0.6302
## 9 55.6263 nan 0.0100 0.6523
## 10 54.9537 nan 0.0100 0.6061
## 20 48.9212 nan 0.0100 0.5676
## 40 39.5169 nan 0.0100 0.3862
## 60 32.2676 nan 0.0100 0.3032
## 80 26.9242 nan 0.0100 0.2374
## 100 22.7466 nan 0.0100 0.1695
## 120 19.5099 nan 0.0100 0.1342
## 140 16.9995 nan 0.0100 0.1033
## 160 14.8824 nan 0.0100 0.0782
## 180 13.2226 nan 0.0100 0.0483
## 200 11.8360 nan 0.0100 0.0641
## 220 10.6695 nan 0.0100 0.0409
## 240 9.6687 nan 0.0100 0.0305
## 260 8.8797 nan 0.0100 0.0286
## 280 8.1734 nan 0.0100 0.0274
## 300 7.5685 nan 0.0100 0.0168
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.0977 nan 0.0100 0.9427
## 2 60.0594 nan 0.0100 1.0613
## 3 59.0918 nan 0.0100 1.0262
## 4 58.1146 nan 0.0100 0.8663
## 5 57.1321 nan 0.0100 0.9276
## 6 56.2013 nan 0.0100 0.9042
## 7 55.3087 nan 0.0100 0.9600
## 8 54.3788 nan 0.0100 0.9244
## 9 53.4878 nan 0.0100 0.8785
## 10 52.6386 nan 0.0100 0.9086
## 20 44.8154 nan 0.0100 0.7210
## 40 32.8350 nan 0.0100 0.4899
## 60 24.4946 nan 0.0100 0.3370
## 80 18.7028 nan 0.0100 0.2310
## 100 14.6219 nan 0.0100 0.1863
## 120 11.5693 nan 0.0100 0.1186
## 140 9.4305 nan 0.0100 0.0736
## 160 7.8682 nan 0.0100 0.0646
## 180 6.6711 nan 0.0100 0.0428
## 200 5.7766 nan 0.0100 0.0313
## 220 5.1078 nan 0.0100 0.0212
## 240 4.6183 nan 0.0100 0.0042
## 260 4.2265 nan 0.0100 0.0123
## 280 3.9328 nan 0.0100 0.0074
## 300 3.6914 nan 0.0100 0.0074
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.1732 nan 0.0100 0.9445
## 2 60.1957 nan 0.0100 0.9623
## 3 59.1990 nan 0.0100 0.8884
## 4 58.2403 nan 0.0100 0.9414
## 5 57.3062 nan 0.0100 0.8516
## 6 56.3683 nan 0.0100 0.8758
## 7 55.4381 nan 0.0100 0.9874
## 8 54.5488 nan 0.0100 0.8425
## 9 53.6730 nan 0.0100 0.8442
## 10 52.7985 nan 0.0100 0.8567
## 20 44.9028 nan 0.0100 0.6723
## 40 32.8287 nan 0.0100 0.5122
## 60 24.4930 nan 0.0100 0.3126
## 80 18.7801 nan 0.0100 0.2517
## 100 14.7229 nan 0.0100 0.1706
## 120 11.7417 nan 0.0100 0.1077
## 140 9.5099 nan 0.0100 0.0873
## 160 7.9306 nan 0.0100 0.0537
## 180 6.7694 nan 0.0100 0.0473
## 200 5.8805 nan 0.0100 0.0343
## 220 5.2281 nan 0.0100 0.0260
## 240 4.7385 nan 0.0100 0.0115
## 260 4.3735 nan 0.0100 0.0150
## 280 4.0978 nan 0.0100 0.0072
## 300 3.8860 nan 0.0100 0.0060
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.1186 nan 0.0100 1.0299
## 2 60.0822 nan 0.0100 0.9548
## 3 59.1150 nan 0.0100 0.9146
## 4 58.1126 nan 0.0100 1.0974
## 5 57.1264 nan 0.0100 0.8615
## 6 56.1828 nan 0.0100 0.8528
## 7 55.3212 nan 0.0100 0.8592
## 8 54.4465 nan 0.0100 0.8176
## 9 53.5818 nan 0.0100 0.8348
## 10 52.6712 nan 0.0100 0.9044
## 20 44.7625 nan 0.0100 0.7289
## 40 32.8066 nan 0.0100 0.4794
## 60 24.4810 nan 0.0100 0.3705
## 80 18.6729 nan 0.0100 0.1756
## 100 14.6467 nan 0.0100 0.1584
## 120 11.7640 nan 0.0100 0.1256
## 140 9.6319 nan 0.0100 0.0743
## 160 8.0970 nan 0.0100 0.0634
## 180 6.9714 nan 0.0100 0.0409
## 200 6.0629 nan 0.0100 0.0347
## 220 5.3935 nan 0.0100 0.0242
## 240 4.8824 nan 0.0100 0.0170
## 260 4.5032 nan 0.0100 0.0138
## 280 4.2157 nan 0.0100 0.0069
## 300 4.0079 nan 0.0100 0.0055
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.0181 nan 0.0100 1.1632
## 2 59.9494 nan 0.0100 0.9765
## 3 58.8971 nan 0.0100 0.9996
## 4 57.8735 nan 0.0100 1.0641
## 5 56.8328 nan 0.0100 1.0347
## 6 55.7976 nan 0.0100 0.9063
## 7 54.8582 nan 0.0100 0.9821
## 8 53.9091 nan 0.0100 0.8822
## 9 52.9542 nan 0.0100 0.9450
## 10 52.0128 nan 0.0100 0.9283
## 20 43.7734 nan 0.0100 0.7030
## 40 31.2970 nan 0.0100 0.5270
## 60 22.7000 nan 0.0100 0.3558
## 80 16.7886 nan 0.0100 0.2125
## 100 12.7738 nan 0.0100 0.1402
## 120 9.8961 nan 0.0100 0.1199
## 140 7.8415 nan 0.0100 0.0743
## 160 6.4162 nan 0.0100 0.0461
## 180 5.3918 nan 0.0100 0.0276
## 200 4.6298 nan 0.0100 0.0222
## 220 4.0768 nan 0.0100 0.0169
## 240 3.6884 nan 0.0100 0.0095
## 260 3.3903 nan 0.0100 0.0057
## 280 3.1447 nan 0.0100 0.0033
## 300 2.9599 nan 0.0100 -0.0021
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.0860 nan 0.0100 1.0078
## 2 59.9509 nan 0.0100 0.8955
## 3 58.8792 nan 0.0100 0.9906
## 4 57.8356 nan 0.0100 0.9603
## 5 56.8157 nan 0.0100 0.9592
## 6 55.8189 nan 0.0100 0.9664
## 7 54.8035 nan 0.0100 0.9628
## 8 53.8682 nan 0.0100 0.8291
## 9 52.9132 nan 0.0100 1.0020
## 10 51.9945 nan 0.0100 0.7970
## 20 43.8395 nan 0.0100 0.7837
## 40 31.5341 nan 0.0100 0.5718
## 60 22.8647 nan 0.0100 0.2933
## 80 17.0311 nan 0.0100 0.2382
## 100 12.9416 nan 0.0100 0.1413
## 120 10.1329 nan 0.0100 0.1129
## 140 8.1037 nan 0.0100 0.0748
## 160 6.6616 nan 0.0100 0.0520
## 180 5.5995 nan 0.0100 0.0487
## 200 4.8319 nan 0.0100 0.0249
## 220 4.2943 nan 0.0100 0.0135
## 240 3.8933 nan 0.0100 0.0162
## 260 3.6135 nan 0.0100 0.0003
## 280 3.3702 nan 0.0100 0.0020
## 300 3.1901 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.0997 nan 0.0100 1.1448
## 2 60.0816 nan 0.0100 1.0968
## 3 59.1176 nan 0.0100 1.0778
## 4 58.0978 nan 0.0100 1.0203
## 5 57.0813 nan 0.0100 1.0607
## 6 56.0957 nan 0.0100 0.9857
## 7 55.1123 nan 0.0100 0.9380
## 8 54.2166 nan 0.0100 0.9265
## 9 53.2753 nan 0.0100 0.8730
## 10 52.3434 nan 0.0100 0.9844
## 20 44.2088 nan 0.0100 0.6842
## 40 31.6618 nan 0.0100 0.5597
## 60 23.2728 nan 0.0100 0.3441
## 80 17.3936 nan 0.0100 0.2397
## 100 13.2763 nan 0.0100 0.1328
## 120 10.4387 nan 0.0100 0.1139
## 140 8.3681 nan 0.0100 0.0963
## 160 6.9513 nan 0.0100 0.0513
## 180 5.9267 nan 0.0100 0.0384
## 200 5.1747 nan 0.0100 0.0271
## 220 4.6475 nan 0.0100 0.0155
## 240 4.2646 nan 0.0100 0.0093
## 260 3.9615 nan 0.0100 0.0075
## 280 3.7386 nan 0.0100 -0.0005
## 300 3.5676 nan 0.0100 -0.0014
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.1881 nan 0.0500 3.8208
## 2 54.5427 nan 0.0500 3.4755
## 3 51.4488 nan 0.0500 3.0136
## 4 48.6118 nan 0.0500 3.0824
## 5 45.8034 nan 0.0500 2.7056
## 6 43.4423 nan 0.0500 2.1932
## 7 41.2264 nan 0.0500 2.2131
## 8 39.1029 nan 0.0500 1.7811
## 9 37.1368 nan 0.0500 2.2201
## 10 35.3039 nan 0.0500 1.6741
## 20 23.1465 nan 0.0500 0.7675
## 40 11.6412 nan 0.0500 0.3018
## 60 7.5487 nan 0.0500 0.0720
## 80 5.5792 nan 0.0500 0.0561
## 100 4.5669 nan 0.0500 0.0096
## 120 4.0937 nan 0.0500 0.0067
## 140 3.8254 nan 0.0500 -0.0052
## 160 3.6798 nan 0.0500 -0.0035
## 180 3.5808 nan 0.0500 -0.0064
## 200 3.4919 nan 0.0500 -0.0052
## 220 3.4185 nan 0.0500 -0.0022
## 240 3.3672 nan 0.0500 -0.0002
## 260 3.3115 nan 0.0500 -0.0156
## 280 3.2659 nan 0.0500 -0.0050
## 300 3.2192 nan 0.0500 -0.0146
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.0602 nan 0.0500 3.8358
## 2 54.4010 nan 0.0500 3.2404
## 3 51.3143 nan 0.0500 2.8677
## 4 48.0191 nan 0.0500 2.7982
## 5 45.3547 nan 0.0500 2.5488
## 6 43.0056 nan 0.0500 2.4537
## 7 40.5589 nan 0.0500 2.1190
## 8 38.4296 nan 0.0500 1.9977
## 9 36.2655 nan 0.0500 1.7318
## 10 34.5600 nan 0.0500 1.7133
## 20 22.3853 nan 0.0500 1.0161
## 40 11.7448 nan 0.0500 0.2635
## 60 7.4733 nan 0.0500 0.0369
## 80 5.5585 nan 0.0500 0.0248
## 100 4.6152 nan 0.0500 0.0273
## 120 4.1724 nan 0.0500 0.0042
## 140 3.9429 nan 0.0500 -0.0043
## 160 3.8223 nan 0.0500 -0.0179
## 180 3.7057 nan 0.0500 -0.0103
## 200 3.6266 nan 0.0500 -0.0099
## 220 3.5412 nan 0.0500 -0.0090
## 240 3.4828 nan 0.0500 0.0007
## 260 3.4252 nan 0.0500 -0.0005
## 280 3.3829 nan 0.0500 -0.0073
## 300 3.3251 nan 0.0500 -0.0038
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.2653 nan 0.0500 3.9766
## 2 54.8815 nan 0.0500 3.1078
## 3 51.8309 nan 0.0500 3.3556
## 4 48.7801 nan 0.0500 2.8763
## 5 46.2317 nan 0.0500 2.6096
## 6 43.7682 nan 0.0500 2.3220
## 7 41.3816 nan 0.0500 2.2582
## 8 39.2572 nan 0.0500 2.0708
## 9 37.0796 nan 0.0500 2.2355
## 10 35.2031 nan 0.0500 1.9405
## 20 22.6753 nan 0.0500 0.7557
## 40 12.0534 nan 0.0500 0.2027
## 60 7.6379 nan 0.0500 0.1136
## 80 5.7225 nan 0.0500 0.0519
## 100 4.8653 nan 0.0500 -0.0086
## 120 4.4377 nan 0.0500 -0.0108
## 140 4.2375 nan 0.0500 0.0003
## 160 4.1094 nan 0.0500 -0.0111
## 180 4.0002 nan 0.0500 -0.0061
## 200 3.9106 nan 0.0500 -0.0020
## 220 3.8325 nan 0.0500 -0.0031
## 240 3.7628 nan 0.0500 -0.0103
## 260 3.7043 nan 0.0500 -0.0033
## 280 3.6272 nan 0.0500 -0.0048
## 300 3.5592 nan 0.0500 -0.0094
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.0438 nan 0.0500 4.5934
## 2 52.4670 nan 0.0500 4.2808
## 3 48.1446 nan 0.0500 3.7412
## 4 44.4282 nan 0.0500 3.9592
## 5 40.9729 nan 0.0500 3.3759
## 6 37.9089 nan 0.0500 3.0781
## 7 35.0891 nan 0.0500 2.8219
## 8 32.4289 nan 0.0500 2.7141
## 9 29.8832 nan 0.0500 2.4005
## 10 27.6554 nan 0.0500 1.8928
## 20 14.3098 nan 0.0500 0.8156
## 40 5.8797 nan 0.0500 0.1527
## 60 3.7304 nan 0.0500 0.0196
## 80 3.1475 nan 0.0500 -0.0154
## 100 2.8373 nan 0.0500 -0.0043
## 120 2.6050 nan 0.0500 -0.0210
## 140 2.4566 nan 0.0500 -0.0091
## 160 2.3281 nan 0.0500 -0.0029
## 180 2.1866 nan 0.0500 -0.0067
## 200 2.0541 nan 0.0500 -0.0039
## 220 1.9187 nan 0.0500 -0.0061
## 240 1.8218 nan 0.0500 -0.0109
## 260 1.7311 nan 0.0500 -0.0104
## 280 1.6543 nan 0.0500 -0.0069
## 300 1.5843 nan 0.0500 -0.0087
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.8417 nan 0.0500 4.5411
## 2 52.3757 nan 0.0500 4.3217
## 3 48.1222 nan 0.0500 3.7584
## 4 44.3999 nan 0.0500 3.6053
## 5 41.0076 nan 0.0500 3.0017
## 6 37.8972 nan 0.0500 3.2062
## 7 35.0140 nan 0.0500 2.8904
## 8 32.3628 nan 0.0500 2.6887
## 9 29.9268 nan 0.0500 2.1858
## 10 27.7878 nan 0.0500 1.7528
## 20 14.4872 nan 0.0500 0.9270
## 40 5.7867 nan 0.0500 0.1248
## 60 3.8092 nan 0.0500 0.0091
## 80 3.2938 nan 0.0500 -0.0076
## 100 2.9802 nan 0.0500 -0.0080
## 120 2.7779 nan 0.0500 -0.0020
## 140 2.6243 nan 0.0500 -0.0292
## 160 2.4941 nan 0.0500 -0.0200
## 180 2.3758 nan 0.0500 -0.0170
## 200 2.2624 nan 0.0500 -0.0123
## 220 2.1762 nan 0.0500 -0.0141
## 240 2.0804 nan 0.0500 -0.0096
## 260 2.0210 nan 0.0500 -0.0121
## 280 1.9341 nan 0.0500 -0.0117
## 300 1.8632 nan 0.0500 -0.0080
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.4354 nan 0.0500 4.9324
## 2 52.6856 nan 0.0500 4.5665
## 3 48.5819 nan 0.0500 3.7521
## 4 44.7524 nan 0.0500 4.1088
## 5 41.1739 nan 0.0500 3.5443
## 6 38.1425 nan 0.0500 3.2636
## 7 35.2090 nan 0.0500 2.7641
## 8 32.4684 nan 0.0500 2.6513
## 9 29.8403 nan 0.0500 2.4102
## 10 27.6731 nan 0.0500 2.2611
## 20 14.3729 nan 0.0500 0.8711
## 40 6.1089 nan 0.0500 0.1803
## 60 4.1491 nan 0.0500 0.0240
## 80 3.5431 nan 0.0500 -0.0110
## 100 3.2463 nan 0.0500 -0.0157
## 120 3.0362 nan 0.0500 -0.0111
## 140 2.8519 nan 0.0500 -0.0163
## 160 2.7025 nan 0.0500 -0.0193
## 180 2.5846 nan 0.0500 -0.0037
## 200 2.4819 nan 0.0500 -0.0041
## 220 2.3853 nan 0.0500 -0.0085
## 240 2.2949 nan 0.0500 -0.0092
## 260 2.2406 nan 0.0500 -0.0161
## 280 2.1830 nan 0.0500 -0.0093
## 300 2.1130 nan 0.0500 -0.0128
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.7797 nan 0.0500 5.3080
## 2 51.7221 nan 0.0500 4.7184
## 3 47.3592 nan 0.0500 4.2967
## 4 43.3355 nan 0.0500 3.5109
## 5 39.6401 nan 0.0500 3.7149
## 6 36.5652 nan 0.0500 3.2352
## 7 33.4771 nan 0.0500 2.6516
## 8 30.9219 nan 0.0500 2.1424
## 9 28.2716 nan 0.0500 2.6244
## 10 26.0370 nan 0.0500 2.4572
## 20 12.4302 nan 0.0500 0.7796
## 40 4.5469 nan 0.0500 0.1346
## 60 2.9627 nan 0.0500 -0.0285
## 80 2.4488 nan 0.0500 -0.0064
## 100 2.1305 nan 0.0500 0.0015
## 120 1.9266 nan 0.0500 -0.0264
## 140 1.7322 nan 0.0500 -0.0261
## 160 1.5931 nan 0.0500 -0.0223
## 180 1.4557 nan 0.0500 -0.0110
## 200 1.3342 nan 0.0500 -0.0044
## 220 1.2308 nan 0.0500 -0.0125
## 240 1.1412 nan 0.0500 -0.0086
## 260 1.0518 nan 0.0500 -0.0058
## 280 0.9758 nan 0.0500 -0.0045
## 300 0.9112 nan 0.0500 -0.0121
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.8052 nan 0.0500 5.1934
## 2 51.9576 nan 0.0500 4.7822
## 3 47.6191 nan 0.0500 4.2905
## 4 43.7178 nan 0.0500 4.0496
## 5 39.9553 nan 0.0500 3.4817
## 6 36.8328 nan 0.0500 3.2097
## 7 33.8109 nan 0.0500 3.1067
## 8 31.0541 nan 0.0500 2.6924
## 9 28.7235 nan 0.0500 2.2073
## 10 26.4528 nan 0.0500 2.2677
## 20 12.5358 nan 0.0500 0.8226
## 40 4.6955 nan 0.0500 0.1092
## 60 3.1561 nan 0.0500 0.0207
## 80 2.6748 nan 0.0500 -0.0184
## 100 2.4147 nan 0.0500 -0.0238
## 120 2.2434 nan 0.0500 -0.0111
## 140 2.0750 nan 0.0500 -0.0276
## 160 1.9281 nan 0.0500 -0.0150
## 180 1.7972 nan 0.0500 -0.0111
## 200 1.6919 nan 0.0500 -0.0111
## 220 1.5931 nan 0.0500 -0.0073
## 240 1.5182 nan 0.0500 -0.0117
## 260 1.4274 nan 0.0500 -0.0136
## 280 1.3609 nan 0.0500 -0.0040
## 300 1.2874 nan 0.0500 -0.0084
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.8488 nan 0.0500 5.2633
## 2 52.1349 nan 0.0500 5.0083
## 3 47.7429 nan 0.0500 4.3664
## 4 43.6825 nan 0.0500 3.5465
## 5 40.0605 nan 0.0500 3.6206
## 6 36.7007 nan 0.0500 2.8452
## 7 33.6879 nan 0.0500 2.6694
## 8 30.9395 nan 0.0500 2.5053
## 9 28.5009 nan 0.0500 2.2607
## 10 26.3635 nan 0.0500 1.9661
## 20 12.9831 nan 0.0500 0.9210
## 40 5.0551 nan 0.0500 0.1290
## 60 3.5914 nan 0.0500 0.0214
## 80 3.0735 nan 0.0500 -0.0181
## 100 2.8128 nan 0.0500 -0.0209
## 120 2.5975 nan 0.0500 -0.0056
## 140 2.4276 nan 0.0500 -0.0124
## 160 2.2774 nan 0.0500 -0.0072
## 180 2.1701 nan 0.0500 -0.0080
## 200 2.0776 nan 0.0500 -0.0096
## 220 1.9822 nan 0.0500 -0.0170
## 240 1.8783 nan 0.0500 -0.0061
## 260 1.8112 nan 0.0500 -0.0115
## 280 1.7343 nan 0.0500 -0.0112
## 300 1.6591 nan 0.0500 -0.0067
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.8108 nan 0.1000 7.5007
## 2 48.0209 nan 0.1000 6.3378
## 3 43.1623 nan 0.1000 5.0967
## 4 38.6243 nan 0.1000 3.6919
## 5 34.6969 nan 0.1000 3.4864
## 6 31.6103 nan 0.1000 2.6766
## 7 28.7867 nan 0.1000 2.7828
## 8 26.4870 nan 0.1000 1.9932
## 9 23.9992 nan 0.1000 2.3550
## 10 22.0016 nan 0.1000 1.8610
## 20 11.0804 nan 0.1000 0.5230
## 40 5.3736 nan 0.1000 0.0995
## 60 4.1375 nan 0.1000 -0.0190
## 80 3.7725 nan 0.1000 -0.0368
## 100 3.5996 nan 0.1000 -0.0025
## 120 3.4697 nan 0.1000 -0.0097
## 140 3.3533 nan 0.1000 -0.0339
## 160 3.2347 nan 0.1000 -0.0025
## 180 3.1362 nan 0.1000 -0.0062
## 200 3.0574 nan 0.1000 -0.0078
## 220 2.9869 nan 0.1000 -0.0235
## 240 2.9183 nan 0.1000 -0.0226
## 260 2.8669 nan 0.1000 -0.0151
## 280 2.8035 nan 0.1000 -0.0129
## 300 2.7439 nan 0.1000 -0.0238
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.3443 nan 0.1000 6.8967
## 2 48.0079 nan 0.1000 5.9306
## 3 43.0792 nan 0.1000 4.6852
## 4 38.6414 nan 0.1000 4.3885
## 5 34.9396 nan 0.1000 3.2151
## 6 31.6789 nan 0.1000 2.4089
## 7 28.5329 nan 0.1000 3.0761
## 8 26.0846 nan 0.1000 2.2409
## 9 23.9617 nan 0.1000 2.0799
## 10 22.2316 nan 0.1000 1.8288
## 20 11.5716 nan 0.1000 0.3759
## 40 5.4864 nan 0.1000 0.0303
## 60 4.2544 nan 0.1000 0.0024
## 80 3.9044 nan 0.1000 -0.0193
## 100 3.7197 nan 0.1000 -0.0127
## 120 3.5601 nan 0.1000 -0.0130
## 140 3.4519 nan 0.1000 -0.0144
## 160 3.3760 nan 0.1000 -0.0096
## 180 3.3122 nan 0.1000 -0.0228
## 200 3.2220 nan 0.1000 -0.0170
## 220 3.1553 nan 0.1000 -0.0140
## 240 3.1171 nan 0.1000 -0.0422
## 260 3.0352 nan 0.1000 -0.0182
## 280 2.9947 nan 0.1000 -0.0119
## 300 2.9614 nan 0.1000 -0.0023
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.7861 nan 0.1000 7.5744
## 2 49.2234 nan 0.1000 5.6367
## 3 44.1796 nan 0.1000 4.2563
## 4 38.9259 nan 0.1000 5.0769
## 5 34.7983 nan 0.1000 3.8742
## 6 31.5173 nan 0.1000 3.5578
## 7 28.8699 nan 0.1000 2.6807
## 8 26.3956 nan 0.1000 2.2813
## 9 23.9863 nan 0.1000 1.9707
## 10 22.0448 nan 0.1000 1.8542
## 20 11.3213 nan 0.1000 0.5519
## 40 5.7017 nan 0.1000 0.0397
## 60 4.5587 nan 0.1000 0.0324
## 80 4.1787 nan 0.1000 -0.0074
## 100 3.9631 nan 0.1000 -0.0009
## 120 3.8200 nan 0.1000 -0.0306
## 140 3.7215 nan 0.1000 -0.0309
## 160 3.6270 nan 0.1000 -0.0148
## 180 3.5313 nan 0.1000 -0.0176
## 200 3.4447 nan 0.1000 -0.0205
## 220 3.3815 nan 0.1000 -0.0170
## 240 3.3005 nan 0.1000 -0.0038
## 260 3.2531 nan 0.1000 -0.0163
## 280 3.1755 nan 0.1000 -0.0069
## 300 3.1208 nan 0.1000 -0.0065
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.3223 nan 0.1000 8.8464
## 2 44.6657 nan 0.1000 7.3540
## 3 38.1758 nan 0.1000 6.7436
## 4 32.4947 nan 0.1000 5.6730
## 5 27.7318 nan 0.1000 4.9662
## 6 23.9998 nan 0.1000 3.4460
## 7 20.8132 nan 0.1000 3.0447
## 8 18.1872 nan 0.1000 2.6695
## 9 15.9176 nan 0.1000 2.1656
## 10 14.1803 nan 0.1000 1.7077
## 20 5.8587 nan 0.1000 0.2893
## 40 3.0812 nan 0.1000 -0.0118
## 60 2.5710 nan 0.1000 -0.0556
## 80 2.2303 nan 0.1000 -0.0287
## 100 1.9870 nan 0.1000 -0.0142
## 120 1.8109 nan 0.1000 -0.0182
## 140 1.6438 nan 0.1000 -0.0106
## 160 1.5217 nan 0.1000 -0.0321
## 180 1.4113 nan 0.1000 -0.0318
## 200 1.3098 nan 0.1000 -0.0207
## 220 1.2040 nan 0.1000 -0.0293
## 240 1.1049 nan 0.1000 -0.0143
## 260 1.0161 nan 0.1000 -0.0049
## 280 0.9439 nan 0.1000 -0.0135
## 300 0.8844 nan 0.1000 -0.0166
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.4021 nan 0.1000 9.0083
## 2 44.2496 nan 0.1000 8.2822
## 3 37.3361 nan 0.1000 5.9655
## 4 31.9045 nan 0.1000 5.0172
## 5 27.4969 nan 0.1000 4.2389
## 6 23.5336 nan 0.1000 3.3599
## 7 20.5659 nan 0.1000 2.9698
## 8 17.9630 nan 0.1000 2.7153
## 9 15.7517 nan 0.1000 2.0936
## 10 14.0820 nan 0.1000 1.5736
## 20 5.7109 nan 0.1000 0.2164
## 40 3.4308 nan 0.1000 -0.0207
## 60 2.8753 nan 0.1000 -0.0364
## 80 2.4971 nan 0.1000 -0.0164
## 100 2.2875 nan 0.1000 -0.0254
## 120 2.1093 nan 0.1000 -0.0271
## 140 1.9871 nan 0.1000 -0.0355
## 160 1.8746 nan 0.1000 -0.0181
## 180 1.7703 nan 0.1000 -0.0229
## 200 1.6605 nan 0.1000 -0.0157
## 220 1.5468 nan 0.1000 -0.0037
## 240 1.4602 nan 0.1000 -0.0220
## 260 1.3786 nan 0.1000 -0.0156
## 280 1.3131 nan 0.1000 -0.0282
## 300 1.2479 nan 0.1000 -0.0094
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.5432 nan 0.1000 8.9100
## 2 44.1516 nan 0.1000 7.9868
## 3 37.7469 nan 0.1000 6.9758
## 4 32.0433 nan 0.1000 5.1038
## 5 27.6694 nan 0.1000 4.1162
## 6 23.9474 nan 0.1000 3.7969
## 7 20.6435 nan 0.1000 2.9436
## 8 17.7614 nan 0.1000 2.3296
## 9 15.7301 nan 0.1000 2.2101
## 10 13.9316 nan 0.1000 1.6741
## 20 5.9425 nan 0.1000 0.2400
## 40 3.7335 nan 0.1000 -0.0056
## 60 3.1698 nan 0.1000 -0.0241
## 80 2.9054 nan 0.1000 -0.0358
## 100 2.6041 nan 0.1000 -0.0406
## 120 2.4028 nan 0.1000 -0.0254
## 140 2.2637 nan 0.1000 -0.0225
## 160 2.1652 nan 0.1000 -0.0313
## 180 2.0107 nan 0.1000 -0.0104
## 200 1.9042 nan 0.1000 -0.0235
## 220 1.8182 nan 0.1000 -0.0268
## 240 1.7075 nan 0.1000 -0.0145
## 260 1.6253 nan 0.1000 -0.0193
## 280 1.5627 nan 0.1000 -0.0164
## 300 1.4935 nan 0.1000 -0.0174
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.9336 nan 0.1000 10.3367
## 2 43.6281 nan 0.1000 8.0883
## 3 36.3628 nan 0.1000 7.6088
## 4 30.5856 nan 0.1000 5.5530
## 5 25.8329 nan 0.1000 4.8506
## 6 21.9819 nan 0.1000 3.9893
## 7 18.6486 nan 0.1000 2.6236
## 8 15.9182 nan 0.1000 2.6920
## 9 13.5781 nan 0.1000 2.1532
## 10 11.8844 nan 0.1000 1.5916
## 20 4.5107 nan 0.1000 0.3219
## 40 2.5758 nan 0.1000 -0.0362
## 60 2.0615 nan 0.1000 -0.0524
## 80 1.6985 nan 0.1000 -0.0403
## 100 1.4105 nan 0.1000 -0.0276
## 120 1.2082 nan 0.1000 -0.0308
## 140 1.0267 nan 0.1000 -0.0264
## 160 0.8917 nan 0.1000 -0.0163
## 180 0.7778 nan 0.1000 -0.0115
## 200 0.6890 nan 0.1000 -0.0143
## 220 0.6099 nan 0.1000 -0.0086
## 240 0.5326 nan 0.1000 -0.0082
## 260 0.4704 nan 0.1000 -0.0084
## 280 0.4184 nan 0.1000 -0.0099
## 300 0.3751 nan 0.1000 -0.0041
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.9032 nan 0.1000 11.0510
## 2 43.4474 nan 0.1000 6.7246
## 3 36.5066 nan 0.1000 6.7035
## 4 30.9613 nan 0.1000 5.5813
## 5 26.3727 nan 0.1000 4.8901
## 6 22.2448 nan 0.1000 3.7377
## 7 19.1221 nan 0.1000 2.8910
## 8 16.6911 nan 0.1000 2.3823
## 9 14.4348 nan 0.1000 1.9521
## 10 12.4688 nan 0.1000 1.6485
## 20 4.6909 nan 0.1000 0.2618
## 40 2.7556 nan 0.1000 -0.0361
## 60 2.2479 nan 0.1000 -0.0555
## 80 1.9144 nan 0.1000 -0.0355
## 100 1.6718 nan 0.1000 -0.0154
## 120 1.4829 nan 0.1000 -0.0251
## 140 1.3412 nan 0.1000 -0.0173
## 160 1.2034 nan 0.1000 -0.0212
## 180 1.1210 nan 0.1000 -0.0208
## 200 1.0161 nan 0.1000 -0.0135
## 220 0.9270 nan 0.1000 -0.0145
## 240 0.8585 nan 0.1000 -0.0190
## 260 0.8004 nan 0.1000 -0.0126
## 280 0.7373 nan 0.1000 -0.0092
## 300 0.6856 nan 0.1000 -0.0155
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.7761 nan 0.1000 9.3610
## 2 42.7726 nan 0.1000 8.9823
## 3 35.9887 nan 0.1000 6.6535
## 4 30.5143 nan 0.1000 5.0268
## 5 25.9943 nan 0.1000 3.8989
## 6 22.1294 nan 0.1000 3.6441
## 7 18.8914 nan 0.1000 3.0980
## 8 16.2929 nan 0.1000 2.3696
## 9 14.1484 nan 0.1000 2.1980
## 10 12.4185 nan 0.1000 1.6913
## 20 4.7602 nan 0.1000 0.1792
## 40 2.9744 nan 0.1000 -0.0121
## 60 2.5793 nan 0.1000 -0.0458
## 80 2.2736 nan 0.1000 -0.0357
## 100 2.0470 nan 0.1000 -0.0203
## 120 1.8663 nan 0.1000 -0.0388
## 140 1.7058 nan 0.1000 -0.0197
## 160 1.5690 nan 0.1000 -0.0272
## 180 1.4434 nan 0.1000 -0.0092
## 200 1.3260 nan 0.1000 -0.0100
## 220 1.2618 nan 0.1000 -0.0237
## 240 1.1755 nan 0.1000 -0.0068
## 260 1.0905 nan 0.1000 -0.0111
## 280 1.0337 nan 0.1000 -0.0177
## 300 0.9815 nan 0.1000 -0.0159
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.9985 nan 0.0100 0.7875
## 2 62.2174 nan 0.0100 0.8430
## 3 61.4593 nan 0.0100 0.7321
## 4 60.6916 nan 0.0100 0.7277
## 5 59.9171 nan 0.0100 0.6648
## 6 59.1093 nan 0.0100 0.7167
## 7 58.3689 nan 0.0100 0.7277
## 8 57.6622 nan 0.0100 0.7277
## 9 57.0034 nan 0.0100 0.6817
## 10 56.3371 nan 0.0100 0.6682
## 20 50.0316 nan 0.0100 0.5674
## 40 40.3489 nan 0.0100 0.4168
## 60 32.9237 nan 0.0100 0.2933
## 80 27.3044 nan 0.0100 0.2460
## 100 22.9947 nan 0.0100 0.1983
## 120 19.5744 nan 0.0100 0.1364
## 140 16.9284 nan 0.0100 0.0978
## 160 14.8381 nan 0.0100 0.0549
## 180 13.1188 nan 0.0100 0.0543
## 200 11.7126 nan 0.0100 0.0510
## 220 10.5697 nan 0.0100 0.0421
## 240 9.6024 nan 0.0100 0.0335
## 260 8.7650 nan 0.0100 0.0252
## 280 8.0587 nan 0.0100 0.0262
## 300 7.4808 nan 0.0100 0.0129
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.9728 nan 0.0100 0.8075
## 2 62.2199 nan 0.0100 0.6267
## 3 61.3663 nan 0.0100 0.8007
## 4 60.5435 nan 0.0100 0.7518
## 5 59.7788 nan 0.0100 0.7242
## 6 59.0246 nan 0.0100 0.7037
## 7 58.2770 nan 0.0100 0.6863
## 8 57.6106 nan 0.0100 0.6865
## 9 56.8931 nan 0.0100 0.7189
## 10 56.1347 nan 0.0100 0.6958
## 20 49.9446 nan 0.0100 0.5677
## 40 40.1068 nan 0.0100 0.3826
## 60 32.7367 nan 0.0100 0.3046
## 80 27.2530 nan 0.0100 0.1204
## 100 22.9359 nan 0.0100 0.1727
## 120 19.6452 nan 0.0100 0.1264
## 140 17.0291 nan 0.0100 0.0933
## 160 14.8957 nan 0.0100 0.0621
## 180 13.1704 nan 0.0100 0.0646
## 200 11.7834 nan 0.0100 0.0420
## 220 10.6054 nan 0.0100 0.0453
## 240 9.6261 nan 0.0100 0.0382
## 260 8.7814 nan 0.0100 0.0347
## 280 8.0555 nan 0.0100 0.0203
## 300 7.4630 nan 0.0100 0.0138
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 63.0177 nan 0.0100 0.7986
## 2 62.2306 nan 0.0100 0.7906
## 3 61.4139 nan 0.0100 0.7346
## 4 60.6331 nan 0.0100 0.7934
## 5 59.9006 nan 0.0100 0.7553
## 6 59.1494 nan 0.0100 0.7061
## 7 58.4641 nan 0.0100 0.7200
## 8 57.7236 nan 0.0100 0.6793
## 9 56.9957 nan 0.0100 0.6509
## 10 56.3227 nan 0.0100 0.6699
## 20 50.0636 nan 0.0100 0.4560
## 40 40.2629 nan 0.0100 0.3811
## 60 32.8189 nan 0.0100 0.2948
## 80 27.2182 nan 0.0100 0.2229
## 100 23.0342 nan 0.0100 0.1894
## 120 19.6412 nan 0.0100 0.1346
## 140 17.0362 nan 0.0100 0.1132
## 160 14.9164 nan 0.0100 0.0774
## 180 13.1875 nan 0.0100 0.0667
## 200 11.7668 nan 0.0100 0.0506
## 220 10.6012 nan 0.0100 0.0413
## 240 9.6379 nan 0.0100 0.0366
## 260 8.8481 nan 0.0100 0.0326
## 280 8.1773 nan 0.0100 0.0273
## 300 7.5830 nan 0.0100 0.0212
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.8472 nan 0.0100 1.0580
## 2 61.8151 nan 0.0100 1.0282
## 3 60.7652 nan 0.0100 0.9007
## 4 59.7377 nan 0.0100 0.9919
## 5 58.7778 nan 0.0100 0.9584
## 6 57.8416 nan 0.0100 0.9681
## 7 56.8664 nan 0.0100 0.9437
## 8 55.9463 nan 0.0100 1.0023
## 9 54.9986 nan 0.0100 0.8953
## 10 54.1560 nan 0.0100 0.8761
## 20 46.2454 nan 0.0100 0.8056
## 40 33.9783 nan 0.0100 0.5523
## 60 25.3631 nan 0.0100 0.2939
## 80 19.3733 nan 0.0100 0.2572
## 100 14.9972 nan 0.0100 0.1952
## 120 11.9489 nan 0.0100 0.1146
## 140 9.7948 nan 0.0100 0.0841
## 160 8.1664 nan 0.0100 0.0520
## 180 6.9351 nan 0.0100 0.0401
## 200 6.0076 nan 0.0100 0.0287
## 220 5.3065 nan 0.0100 0.0260
## 240 4.7662 nan 0.0100 0.0192
## 260 4.3874 nan 0.0100 0.0037
## 280 4.0867 nan 0.0100 0.0093
## 300 3.8419 nan 0.0100 0.0078
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.7955 nan 0.0100 1.1215
## 2 61.7435 nan 0.0100 0.9638
## 3 60.6800 nan 0.0100 1.0060
## 4 59.6701 nan 0.0100 0.9358
## 5 58.6875 nan 0.0100 0.9502
## 6 57.7191 nan 0.0100 0.9013
## 7 56.7564 nan 0.0100 0.8782
## 8 55.8102 nan 0.0100 1.0219
## 9 54.8753 nan 0.0100 0.7793
## 10 53.9803 nan 0.0100 0.7803
## 20 45.9157 nan 0.0100 0.7360
## 40 33.6841 nan 0.0100 0.4659
## 60 25.0701 nan 0.0100 0.3866
## 80 19.0979 nan 0.0100 0.2252
## 100 14.8930 nan 0.0100 0.1779
## 120 11.8803 nan 0.0100 0.1228
## 140 9.7416 nan 0.0100 0.0843
## 160 8.1806 nan 0.0100 0.0573
## 180 7.0130 nan 0.0100 0.0458
## 200 6.0820 nan 0.0100 0.0307
## 220 5.4204 nan 0.0100 0.0116
## 240 4.8834 nan 0.0100 0.0197
## 260 4.4943 nan 0.0100 0.0112
## 280 4.2012 nan 0.0100 0.0040
## 300 3.9609 nan 0.0100 0.0024
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.7610 nan 0.0100 1.1142
## 2 61.7814 nan 0.0100 1.0401
## 3 60.7940 nan 0.0100 0.9031
## 4 59.7810 nan 0.0100 0.9792
## 5 58.8219 nan 0.0100 0.9726
## 6 57.8467 nan 0.0100 1.0026
## 7 56.8668 nan 0.0100 0.8839
## 8 55.8837 nan 0.0100 0.8685
## 9 54.9786 nan 0.0100 0.9190
## 10 54.1008 nan 0.0100 0.9809
## 20 46.0141 nan 0.0100 0.7153
## 40 33.8735 nan 0.0100 0.4247
## 60 25.3138 nan 0.0100 0.3529
## 80 19.3380 nan 0.0100 0.2140
## 100 15.1335 nan 0.0100 0.1771
## 120 12.0413 nan 0.0100 0.1170
## 140 9.8881 nan 0.0100 0.0918
## 160 8.2730 nan 0.0100 0.0556
## 180 7.1074 nan 0.0100 0.0431
## 200 6.2181 nan 0.0100 0.0334
## 220 5.4841 nan 0.0100 0.0248
## 240 4.9866 nan 0.0100 0.0164
## 260 4.6185 nan 0.0100 0.0092
## 280 4.3113 nan 0.0100 0.0071
## 300 4.0936 nan 0.0100 0.0082
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.7480 nan 0.0100 0.9854
## 2 61.6227 nan 0.0100 1.1596
## 3 60.5054 nan 0.0100 1.2123
## 4 59.4158 nan 0.0100 0.9801
## 5 58.3669 nan 0.0100 0.9509
## 6 57.3524 nan 0.0100 1.0987
## 7 56.3448 nan 0.0100 1.1202
## 8 55.3708 nan 0.0100 0.7840
## 9 54.4041 nan 0.0100 0.9042
## 10 53.4696 nan 0.0100 0.8723
## 20 45.1079 nan 0.0100 0.7032
## 40 32.2431 nan 0.0100 0.5311
## 60 23.4316 nan 0.0100 0.3352
## 80 17.3572 nan 0.0100 0.2649
## 100 13.0953 nan 0.0100 0.1503
## 120 10.1238 nan 0.0100 0.1069
## 140 8.0787 nan 0.0100 0.0690
## 160 6.5862 nan 0.0100 0.0594
## 180 5.5409 nan 0.0100 0.0293
## 200 4.7941 nan 0.0100 0.0234
## 220 4.2283 nan 0.0100 0.0126
## 240 3.7993 nan 0.0100 0.0093
## 260 3.4848 nan 0.0100 0.0033
## 280 3.2269 nan 0.0100 0.0041
## 300 3.0365 nan 0.0100 -0.0025
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.7756 nan 0.0100 1.1535
## 2 61.6614 nan 0.0100 1.1594
## 3 60.5751 nan 0.0100 1.0897
## 4 59.4898 nan 0.0100 1.1126
## 5 58.4567 nan 0.0100 1.0362
## 6 57.4327 nan 0.0100 0.9636
## 7 56.3995 nan 0.0100 0.9951
## 8 55.3840 nan 0.0100 0.9755
## 9 54.4486 nan 0.0100 0.9077
## 10 53.5274 nan 0.0100 0.8308
## 20 45.1321 nan 0.0100 0.7084
## 40 32.4246 nan 0.0100 0.4667
## 60 23.5740 nan 0.0100 0.3287
## 80 17.5295 nan 0.0100 0.2455
## 100 13.2449 nan 0.0100 0.1571
## 120 10.3244 nan 0.0100 0.1158
## 140 8.2139 nan 0.0100 0.0646
## 160 6.7541 nan 0.0100 0.0378
## 180 5.7101 nan 0.0100 0.0431
## 200 4.9589 nan 0.0100 0.0234
## 220 4.4198 nan 0.0100 0.0154
## 240 4.0180 nan 0.0100 0.0119
## 260 3.7349 nan 0.0100 0.0071
## 280 3.4961 nan 0.0100 0.0057
## 300 3.3209 nan 0.0100 -0.0008
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.7386 nan 0.0100 1.1608
## 2 61.6411 nan 0.0100 1.0759
## 3 60.5095 nan 0.0100 1.0758
## 4 59.4845 nan 0.0100 0.9343
## 5 58.5064 nan 0.0100 1.0502
## 6 57.4671 nan 0.0100 0.9650
## 7 56.4945 nan 0.0100 1.0243
## 8 55.5046 nan 0.0100 0.9557
## 9 54.4976 nan 0.0100 0.8931
## 10 53.5467 nan 0.0100 0.7987
## 20 45.1999 nan 0.0100 0.7831
## 40 32.5786 nan 0.0100 0.5120
## 60 23.7932 nan 0.0100 0.3348
## 80 17.7073 nan 0.0100 0.2131
## 100 13.4632 nan 0.0100 0.1663
## 120 10.5338 nan 0.0100 0.1182
## 140 8.4930 nan 0.0100 0.0772
## 160 7.0194 nan 0.0100 0.0454
## 180 5.9687 nan 0.0100 0.0327
## 200 5.2346 nan 0.0100 0.0320
## 220 4.6896 nan 0.0100 0.0176
## 240 4.2815 nan 0.0100 0.0091
## 260 3.9894 nan 0.0100 0.0070
## 280 3.7589 nan 0.0100 -0.0015
## 300 3.5781 nan 0.0100 0.0057
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.9098 nan 0.0500 4.1048
## 2 56.4271 nan 0.0500 3.6368
## 3 53.0966 nan 0.0500 3.2035
## 4 50.1659 nan 0.0500 3.1309
## 5 47.5503 nan 0.0500 2.6222
## 6 44.7888 nan 0.0500 2.5692
## 7 42.6590 nan 0.0500 2.1013
## 8 40.5665 nan 0.0500 1.9516
## 9 38.4932 nan 0.0500 2.1868
## 10 36.4560 nan 0.0500 1.8213
## 20 23.4781 nan 0.0500 0.9012
## 40 11.8874 nan 0.0500 0.2211
## 60 7.5922 nan 0.0500 0.0636
## 80 5.7229 nan 0.0500 0.0263
## 100 4.7217 nan 0.0500 0.0176
## 120 4.2670 nan 0.0500 -0.0032
## 140 4.0540 nan 0.0500 -0.0170
## 160 3.9030 nan 0.0500 -0.0012
## 180 3.8103 nan 0.0500 -0.0150
## 200 3.7078 nan 0.0500 -0.0027
## 220 3.6273 nan 0.0500 -0.0044
## 240 3.5434 nan 0.0500 -0.0043
## 260 3.4911 nan 0.0500 -0.0070
## 280 3.4394 nan 0.0500 -0.0119
## 300 3.3906 nan 0.0500 -0.0059
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.6441 nan 0.0500 3.7437
## 2 56.0616 nan 0.0500 3.3833
## 3 52.4354 nan 0.0500 3.4142
## 4 49.6572 nan 0.0500 2.7917
## 5 46.9582 nan 0.0500 2.5597
## 6 44.6289 nan 0.0500 2.5130
## 7 42.1087 nan 0.0500 2.4419
## 8 39.9137 nan 0.0500 2.1136
## 9 37.8415 nan 0.0500 2.1188
## 10 35.9150 nan 0.0500 1.7569
## 20 22.4811 nan 0.0500 0.8767
## 40 11.5685 nan 0.0500 0.2598
## 60 7.3333 nan 0.0500 0.1086
## 80 5.4810 nan 0.0500 0.0296
## 100 4.6179 nan 0.0500 0.0190
## 120 4.1811 nan 0.0500 0.0026
## 140 3.9598 nan 0.0500 0.0009
## 160 3.8300 nan 0.0500 -0.0134
## 180 3.7330 nan 0.0500 -0.0015
## 200 3.6539 nan 0.0500 -0.0107
## 220 3.5875 nan 0.0500 -0.0024
## 240 3.5257 nan 0.0500 -0.0077
## 260 3.4681 nan 0.0500 -0.0058
## 280 3.4136 nan 0.0500 -0.0014
## 300 3.3698 nan 0.0500 -0.0084
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.0469 nan 0.0500 4.2440
## 2 56.7843 nan 0.0500 3.6193
## 3 53.5268 nan 0.0500 3.5454
## 4 50.3479 nan 0.0500 2.9587
## 5 47.2906 nan 0.0500 2.7413
## 6 44.6741 nan 0.0500 2.4363
## 7 42.3988 nan 0.0500 2.1610
## 8 40.1805 nan 0.0500 2.2103
## 9 38.0956 nan 0.0500 1.8684
## 10 36.1982 nan 0.0500 1.9085
## 20 22.8428 nan 0.0500 0.9003
## 40 11.7585 nan 0.0500 0.2078
## 60 7.5963 nan 0.0500 0.1208
## 80 5.6870 nan 0.0500 0.0073
## 100 4.8346 nan 0.0500 0.0062
## 120 4.4647 nan 0.0500 0.0001
## 140 4.2605 nan 0.0500 -0.0072
## 160 4.1150 nan 0.0500 0.0017
## 180 4.0121 nan 0.0500 -0.0009
## 200 3.9124 nan 0.0500 -0.0246
## 220 3.8312 nan 0.0500 0.0003
## 240 3.7587 nan 0.0500 -0.0031
## 260 3.6907 nan 0.0500 -0.0183
## 280 3.6319 nan 0.0500 -0.0143
## 300 3.5749 nan 0.0500 -0.0014
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.7636 nan 0.0500 5.1433
## 2 54.0719 nan 0.0500 4.1461
## 3 49.7294 nan 0.0500 4.0655
## 4 45.8498 nan 0.0500 4.0425
## 5 42.2136 nan 0.0500 3.4826
## 6 38.9837 nan 0.0500 3.1701
## 7 36.1836 nan 0.0500 2.4554
## 8 33.5145 nan 0.0500 2.7270
## 9 31.0261 nan 0.0500 2.6203
## 10 28.9104 nan 0.0500 2.2185
## 20 14.5809 nan 0.0500 0.9342
## 40 5.8341 nan 0.0500 0.1041
## 60 3.7838 nan 0.0500 0.0263
## 80 3.1607 nan 0.0500 0.0126
## 100 2.8608 nan 0.0500 -0.0172
## 120 2.6389 nan 0.0500 -0.0127
## 140 2.4352 nan 0.0500 -0.0089
## 160 2.2785 nan 0.0500 -0.0009
## 180 2.1598 nan 0.0500 -0.0006
## 200 2.0275 nan 0.0500 -0.0229
## 220 1.9335 nan 0.0500 -0.0097
## 240 1.8510 nan 0.0500 -0.0099
## 260 1.7582 nan 0.0500 -0.0070
## 280 1.6813 nan 0.0500 -0.0074
## 300 1.6014 nan 0.0500 -0.0134
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.5867 nan 0.0500 5.2170
## 2 54.0468 nan 0.0500 4.2882
## 3 49.8288 nan 0.0500 4.4252
## 4 45.9101 nan 0.0500 3.9567
## 5 42.3846 nan 0.0500 3.6024
## 6 39.3939 nan 0.0500 3.6098
## 7 36.4173 nan 0.0500 3.0475
## 8 33.7597 nan 0.0500 2.5965
## 9 31.1969 nan 0.0500 2.4442
## 10 28.8064 nan 0.0500 2.2393
## 20 14.7728 nan 0.0500 0.7685
## 40 5.9743 nan 0.0500 0.1615
## 60 4.0018 nan 0.0500 0.0378
## 80 3.3948 nan 0.0500 0.0056
## 100 3.0703 nan 0.0500 -0.0164
## 120 2.8332 nan 0.0500 -0.0131
## 140 2.6644 nan 0.0500 -0.0022
## 160 2.5310 nan 0.0500 -0.0085
## 180 2.3943 nan 0.0500 -0.0160
## 200 2.2851 nan 0.0500 -0.0026
## 220 2.1822 nan 0.0500 -0.0140
## 240 2.1083 nan 0.0500 -0.0077
## 260 2.0309 nan 0.0500 -0.0073
## 280 1.9559 nan 0.0500 -0.0088
## 300 1.8858 nan 0.0500 -0.0126
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.8172 nan 0.0500 5.0553
## 2 53.9460 nan 0.0500 4.9119
## 3 49.8320 nan 0.0500 4.3022
## 4 45.7921 nan 0.0500 4.1106
## 5 42.4456 nan 0.0500 3.3215
## 6 39.1334 nan 0.0500 2.8395
## 7 36.0692 nan 0.0500 3.2623
## 8 33.3416 nan 0.0500 2.5261
## 9 30.8669 nan 0.0500 2.3282
## 10 28.5887 nan 0.0500 2.2618
## 20 14.5897 nan 0.0500 0.8836
## 40 6.1577 nan 0.0500 0.1726
## 60 4.1437 nan 0.0500 0.0334
## 80 3.5131 nan 0.0500 0.0210
## 100 3.2283 nan 0.0500 -0.0141
## 120 2.9968 nan 0.0500 -0.0204
## 140 2.8371 nan 0.0500 -0.0056
## 160 2.6838 nan 0.0500 -0.0142
## 180 2.5663 nan 0.0500 -0.0203
## 200 2.4534 nan 0.0500 -0.0067
## 220 2.3746 nan 0.0500 -0.0091
## 240 2.2865 nan 0.0500 -0.0051
## 260 2.2246 nan 0.0500 -0.0157
## 280 2.1581 nan 0.0500 -0.0094
## 300 2.0965 nan 0.0500 -0.0149
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.3393 nan 0.0500 5.1045
## 2 53.2065 nan 0.0500 5.1345
## 3 48.5108 nan 0.0500 4.8570
## 4 44.3641 nan 0.0500 3.6656
## 5 40.9514 nan 0.0500 3.7283
## 6 37.5076 nan 0.0500 3.3830
## 7 34.4473 nan 0.0500 2.9459
## 8 31.6661 nan 0.0500 2.7402
## 9 29.1464 nan 0.0500 2.4814
## 10 26.8289 nan 0.0500 2.0719
## 20 12.9020 nan 0.0500 0.7695
## 40 4.8440 nan 0.0500 0.0998
## 60 3.0419 nan 0.0500 0.0075
## 80 2.5065 nan 0.0500 -0.0242
## 100 2.1707 nan 0.0500 -0.0107
## 120 1.9493 nan 0.0500 -0.0155
## 140 1.7679 nan 0.0500 -0.0155
## 160 1.5994 nan 0.0500 -0.0232
## 180 1.4622 nan 0.0500 -0.0177
## 200 1.3392 nan 0.0500 -0.0166
## 220 1.2215 nan 0.0500 -0.0102
## 240 1.1252 nan 0.0500 -0.0116
## 260 1.0387 nan 0.0500 -0.0144
## 280 0.9759 nan 0.0500 -0.0087
## 300 0.9170 nan 0.0500 -0.0085
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.0569 nan 0.0500 5.8005
## 2 53.2120 nan 0.0500 4.3338
## 3 48.5908 nan 0.0500 4.7339
## 4 44.5846 nan 0.0500 3.8315
## 5 41.0023 nan 0.0500 3.6521
## 6 37.7011 nan 0.0500 3.4177
## 7 34.5523 nan 0.0500 2.4721
## 8 31.7436 nan 0.0500 2.5796
## 9 29.2161 nan 0.0500 2.5167
## 10 27.0388 nan 0.0500 2.0536
## 20 13.1026 nan 0.0500 0.7898
## 40 4.7899 nan 0.0500 0.1368
## 60 3.1979 nan 0.0500 -0.0115
## 80 2.7368 nan 0.0500 0.0016
## 100 2.4480 nan 0.0500 -0.0069
## 120 2.2418 nan 0.0500 -0.0086
## 140 2.0646 nan 0.0500 -0.0132
## 160 1.9299 nan 0.0500 -0.0106
## 180 1.7936 nan 0.0500 -0.0151
## 200 1.6881 nan 0.0500 -0.0099
## 220 1.5813 nan 0.0500 -0.0165
## 240 1.4927 nan 0.0500 -0.0082
## 260 1.4125 nan 0.0500 -0.0106
## 280 1.3407 nan 0.0500 -0.0105
## 300 1.2748 nan 0.0500 -0.0063
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.5613 nan 0.0500 5.1229
## 2 53.7997 nan 0.0500 5.4120
## 3 49.4664 nan 0.0500 4.2559
## 4 45.2781 nan 0.0500 4.2120
## 5 41.3838 nan 0.0500 3.5533
## 6 37.9544 nan 0.0500 3.2361
## 7 34.8002 nan 0.0500 2.7159
## 8 31.8574 nan 0.0500 2.7398
## 9 29.3410 nan 0.0500 2.5587
## 10 27.1484 nan 0.0500 2.0509
## 20 13.0973 nan 0.0500 0.8598
## 40 5.1876 nan 0.0500 0.1188
## 60 3.6646 nan 0.0500 0.0025
## 80 3.1571 nan 0.0500 -0.0155
## 100 2.8687 nan 0.0500 -0.0219
## 120 2.6294 nan 0.0500 -0.0223
## 140 2.4278 nan 0.0500 -0.0298
## 160 2.2907 nan 0.0500 -0.0109
## 180 2.1632 nan 0.0500 -0.0107
## 200 2.0605 nan 0.0500 -0.0187
## 220 1.9530 nan 0.0500 -0.0143
## 240 1.8823 nan 0.0500 -0.0125
## 260 1.8020 nan 0.0500 -0.0163
## 280 1.7263 nan 0.0500 -0.0136
## 300 1.6653 nan 0.0500 -0.0064
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.7107 nan 0.1000 7.1249
## 2 49.9424 nan 0.1000 6.8176
## 3 44.9423 nan 0.1000 5.4342
## 4 40.2198 nan 0.1000 4.5277
## 5 36.0712 nan 0.1000 3.3928
## 6 32.6024 nan 0.1000 3.4584
## 7 29.5447 nan 0.1000 2.7444
## 8 26.9974 nan 0.1000 2.4453
## 9 24.6215 nan 0.1000 2.3974
## 10 22.6000 nan 0.1000 1.5529
## 20 11.6752 nan 0.1000 0.4538
## 40 5.7940 nan 0.1000 0.0529
## 60 4.4432 nan 0.1000 0.0219
## 80 4.0681 nan 0.1000 0.0001
## 100 3.8773 nan 0.1000 -0.0022
## 120 3.6893 nan 0.1000 -0.0028
## 140 3.5515 nan 0.1000 -0.0605
## 160 3.4202 nan 0.1000 -0.0120
## 180 3.3227 nan 0.1000 -0.0218
## 200 3.2451 nan 0.1000 -0.0010
## 220 3.1666 nan 0.1000 -0.0259
## 240 3.0957 nan 0.1000 -0.0099
## 260 3.0286 nan 0.1000 -0.0066
## 280 2.9555 nan 0.1000 -0.0211
## 300 2.9066 nan 0.1000 -0.0321
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.7692 nan 0.1000 7.9460
## 2 49.4909 nan 0.1000 6.1097
## 3 44.2695 nan 0.1000 4.8559
## 4 40.0814 nan 0.1000 3.7968
## 5 36.3905 nan 0.1000 3.8122
## 6 32.8987 nan 0.1000 3.6521
## 7 29.7813 nan 0.1000 2.9491
## 8 27.0564 nan 0.1000 2.7733
## 9 24.4975 nan 0.1000 2.4207
## 10 22.4945 nan 0.1000 1.7237
## 20 11.7907 nan 0.1000 0.4792
## 40 5.7844 nan 0.1000 0.1214
## 60 4.3809 nan 0.1000 0.0073
## 80 4.0432 nan 0.1000 -0.0040
## 100 3.8185 nan 0.1000 -0.0221
## 120 3.6507 nan 0.1000 -0.0031
## 140 3.5547 nan 0.1000 -0.0223
## 160 3.4571 nan 0.1000 -0.0153
## 180 3.3754 nan 0.1000 -0.0173
## 200 3.3178 nan 0.1000 -0.0201
## 220 3.2456 nan 0.1000 -0.0263
## 240 3.1805 nan 0.1000 -0.0181
## 260 3.1171 nan 0.1000 -0.0124
## 280 3.0690 nan 0.1000 -0.0162
## 300 3.0136 nan 0.1000 -0.0227
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.6537 nan 0.1000 7.8991
## 2 50.3931 nan 0.1000 6.2872
## 3 45.0122 nan 0.1000 5.3608
## 4 40.9096 nan 0.1000 3.8877
## 5 36.7605 nan 0.1000 3.7550
## 6 33.2573 nan 0.1000 3.3243
## 7 29.9867 nan 0.1000 2.8839
## 8 27.3614 nan 0.1000 2.2408
## 9 24.9204 nan 0.1000 2.2853
## 10 23.1194 nan 0.1000 1.6966
## 20 11.9908 nan 0.1000 0.5310
## 40 5.8346 nan 0.1000 0.1196
## 60 4.6497 nan 0.1000 -0.0195
## 80 4.2677 nan 0.1000 -0.0409
## 100 4.0744 nan 0.1000 -0.0186
## 120 3.8959 nan 0.1000 -0.0138
## 140 3.7453 nan 0.1000 -0.0338
## 160 3.6214 nan 0.1000 -0.0071
## 180 3.5386 nan 0.1000 -0.0160
## 200 3.4588 nan 0.1000 -0.0171
## 220 3.4076 nan 0.1000 -0.0253
## 240 3.3565 nan 0.1000 -0.0274
## 260 3.2951 nan 0.1000 -0.0206
## 280 3.2426 nan 0.1000 -0.0497
## 300 3.1803 nan 0.1000 -0.0058
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.7873 nan 0.1000 9.1290
## 2 45.7962 nan 0.1000 7.7137
## 3 38.7359 nan 0.1000 7.1249
## 4 32.9892 nan 0.1000 5.7700
## 5 28.4315 nan 0.1000 4.3602
## 6 24.5868 nan 0.1000 3.5410
## 7 21.3789 nan 0.1000 3.1941
## 8 18.4510 nan 0.1000 2.6498
## 9 16.3676 nan 0.1000 2.1125
## 10 14.3417 nan 0.1000 1.7946
## 20 5.8491 nan 0.1000 0.3472
## 40 3.2467 nan 0.1000 0.0056
## 60 2.6828 nan 0.1000 -0.0118
## 80 2.2998 nan 0.1000 -0.0206
## 100 2.0345 nan 0.1000 -0.0233
## 120 1.8409 nan 0.1000 -0.0126
## 140 1.6791 nan 0.1000 -0.0163
## 160 1.5443 nan 0.1000 -0.0178
## 180 1.4157 nan 0.1000 -0.0160
## 200 1.3092 nan 0.1000 -0.0176
## 220 1.2289 nan 0.1000 -0.0090
## 240 1.1451 nan 0.1000 -0.0043
## 260 1.0615 nan 0.1000 -0.0227
## 280 0.9799 nan 0.1000 -0.0122
## 300 0.9200 nan 0.1000 -0.0091
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.7308 nan 0.1000 10.1487
## 2 45.2547 nan 0.1000 8.7644
## 3 38.0509 nan 0.1000 7.3119
## 4 32.2444 nan 0.1000 5.4438
## 5 27.5389 nan 0.1000 4.3646
## 6 23.6937 nan 0.1000 3.3059
## 7 20.4775 nan 0.1000 2.9580
## 8 18.0230 nan 0.1000 2.5625
## 9 15.7928 nan 0.1000 2.2526
## 10 13.9186 nan 0.1000 1.7400
## 20 6.0184 nan 0.1000 0.2870
## 40 3.4837 nan 0.1000 -0.0186
## 60 2.8838 nan 0.1000 -0.0242
## 80 2.5470 nan 0.1000 -0.0324
## 100 2.2953 nan 0.1000 -0.0206
## 120 2.1178 nan 0.1000 -0.0265
## 140 1.9513 nan 0.1000 -0.0276
## 160 1.8246 nan 0.1000 -0.0446
## 180 1.7260 nan 0.1000 -0.0257
## 200 1.6415 nan 0.1000 -0.0203
## 220 1.5431 nan 0.1000 -0.0176
## 240 1.4581 nan 0.1000 -0.0107
## 260 1.3837 nan 0.1000 0.0000
## 280 1.3080 nan 0.1000 -0.0184
## 300 1.2463 nan 0.1000 -0.0138
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.3594 nan 0.1000 9.9251
## 2 44.5474 nan 0.1000 8.3285
## 3 37.8864 nan 0.1000 7.0288
## 4 32.1471 nan 0.1000 5.0124
## 5 27.7939 nan 0.1000 4.6844
## 6 23.9038 nan 0.1000 3.7461
## 7 20.8659 nan 0.1000 3.1656
## 8 18.3206 nan 0.1000 2.2065
## 9 15.8902 nan 0.1000 1.7098
## 10 13.9527 nan 0.1000 1.4113
## 20 6.0328 nan 0.1000 0.3251
## 40 3.4588 nan 0.1000 0.0191
## 60 2.9379 nan 0.1000 -0.0088
## 80 2.6484 nan 0.1000 -0.0270
## 100 2.4853 nan 0.1000 -0.0013
## 120 2.3029 nan 0.1000 -0.0151
## 140 2.1725 nan 0.1000 -0.0332
## 160 2.0444 nan 0.1000 -0.0456
## 180 1.9345 nan 0.1000 -0.0239
## 200 1.8337 nan 0.1000 -0.0175
## 220 1.7398 nan 0.1000 -0.0212
## 240 1.6474 nan 0.1000 -0.0359
## 260 1.5777 nan 0.1000 -0.0213
## 280 1.5068 nan 0.1000 -0.0181
## 300 1.4430 nan 0.1000 -0.0170
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.0479 nan 0.1000 9.8548
## 2 44.5334 nan 0.1000 7.5746
## 3 36.9696 nan 0.1000 7.1489
## 4 31.1362 nan 0.1000 4.9150
## 5 26.3586 nan 0.1000 4.7039
## 6 22.3089 nan 0.1000 3.8827
## 7 19.0925 nan 0.1000 2.8275
## 8 16.4445 nan 0.1000 2.6879
## 9 14.2281 nan 0.1000 2.2221
## 10 12.5914 nan 0.1000 1.6502
## 20 4.7730 nan 0.1000 0.2575
## 40 2.6609 nan 0.1000 -0.0291
## 60 2.0643 nan 0.1000 -0.0378
## 80 1.7014 nan 0.1000 -0.0381
## 100 1.4711 nan 0.1000 -0.0197
## 120 1.2311 nan 0.1000 -0.0223
## 140 1.0535 nan 0.1000 -0.0255
## 160 0.9040 nan 0.1000 -0.0086
## 180 0.7878 nan 0.1000 -0.0129
## 200 0.6791 nan 0.1000 -0.0121
## 220 0.6108 nan 0.1000 -0.0088
## 240 0.5407 nan 0.1000 -0.0070
## 260 0.4872 nan 0.1000 -0.0087
## 280 0.4424 nan 0.1000 -0.0106
## 300 0.3954 nan 0.1000 -0.0063
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.3589 nan 0.1000 9.8892
## 2 44.4164 nan 0.1000 8.4572
## 3 37.4332 nan 0.1000 6.8001
## 4 31.4089 nan 0.1000 6.7308
## 5 26.4813 nan 0.1000 4.5053
## 6 22.7440 nan 0.1000 3.4369
## 7 19.6367 nan 0.1000 2.9010
## 8 16.8183 nan 0.1000 2.8924
## 9 14.5057 nan 0.1000 2.3272
## 10 12.5532 nan 0.1000 1.7684
## 20 4.8839 nan 0.1000 0.2116
## 40 2.9095 nan 0.1000 -0.0104
## 60 2.3911 nan 0.1000 -0.0290
## 80 2.0573 nan 0.1000 -0.0123
## 100 1.8176 nan 0.1000 -0.0357
## 120 1.6304 nan 0.1000 -0.0341
## 140 1.4759 nan 0.1000 -0.0242
## 160 1.3384 nan 0.1000 -0.0333
## 180 1.1946 nan 0.1000 -0.0193
## 200 1.0783 nan 0.1000 -0.0194
## 220 0.9832 nan 0.1000 -0.0214
## 240 0.9016 nan 0.1000 -0.0080
## 260 0.8464 nan 0.1000 -0.0216
## 280 0.7652 nan 0.1000 -0.0049
## 300 0.7142 nan 0.1000 -0.0139
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.8167 nan 0.1000 10.5142
## 2 44.0910 nan 0.1000 8.7777
## 3 36.7865 nan 0.1000 7.6310
## 4 30.8366 nan 0.1000 5.4251
## 5 26.1056 nan 0.1000 4.5183
## 6 22.1096 nan 0.1000 4.1077
## 7 18.8688 nan 0.1000 2.8525
## 8 16.2652 nan 0.1000 2.5781
## 9 14.1881 nan 0.1000 1.7926
## 10 12.6016 nan 0.1000 1.5399
## 20 5.1494 nan 0.1000 0.2840
## 40 3.1300 nan 0.1000 0.0286
## 60 2.6763 nan 0.1000 -0.0292
## 80 2.3678 nan 0.1000 -0.0172
## 100 2.1244 nan 0.1000 -0.0044
## 120 1.9211 nan 0.1000 -0.0059
## 140 1.7671 nan 0.1000 -0.0238
## 160 1.6278 nan 0.1000 -0.0343
## 180 1.5020 nan 0.1000 -0.0219
## 200 1.3941 nan 0.1000 -0.0026
## 220 1.3018 nan 0.1000 -0.0218
## 240 1.2041 nan 0.1000 -0.0102
## 260 1.1194 nan 0.1000 -0.0223
## 280 1.0550 nan 0.1000 -0.0132
## 300 0.9905 nan 0.1000 -0.0189
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.6111 nan 0.0100 0.7336
## 2 59.9035 nan 0.0100 0.7284
## 3 59.1369 nan 0.0100 0.6839
## 4 58.4266 nan 0.0100 0.7272
## 5 57.7643 nan 0.0100 0.6231
## 6 57.0606 nan 0.0100 0.6825
## 7 56.3533 nan 0.0100 0.6824
## 8 55.7011 nan 0.0100 0.6267
## 9 55.0193 nan 0.0100 0.6522
## 10 54.2865 nan 0.0100 0.6374
## 20 48.3009 nan 0.0100 0.5510
## 40 38.8594 nan 0.0100 0.3557
## 60 32.0326 nan 0.0100 0.2656
## 80 26.5393 nan 0.0100 0.2287
## 100 22.4845 nan 0.0100 0.1519
## 120 19.1429 nan 0.0100 0.1216
## 140 16.6190 nan 0.0100 0.1101
## 160 14.5774 nan 0.0100 0.0796
## 180 12.9194 nan 0.0100 0.0660
## 200 11.5409 nan 0.0100 0.0456
## 220 10.3951 nan 0.0100 0.0489
## 240 9.4656 nan 0.0100 0.0342
## 260 8.6582 nan 0.0100 0.0327
## 280 7.9663 nan 0.0100 0.0168
## 300 7.3633 nan 0.0100 0.0260
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.6813 nan 0.0100 0.7310
## 2 59.9931 nan 0.0100 0.7730
## 3 59.2090 nan 0.0100 0.7390
## 4 58.4632 nan 0.0100 0.7210
## 5 57.6812 nan 0.0100 0.7051
## 6 56.9632 nan 0.0100 0.7568
## 7 56.2857 nan 0.0100 0.7171
## 8 55.6027 nan 0.0100 0.6815
## 9 55.0002 nan 0.0100 0.6361
## 10 54.3810 nan 0.0100 0.6466
## 20 48.4453 nan 0.0100 0.5504
## 40 38.9750 nan 0.0100 0.3946
## 60 32.0457 nan 0.0100 0.3000
## 80 26.6680 nan 0.0100 0.1879
## 100 22.5329 nan 0.0100 0.1950
## 120 19.3149 nan 0.0100 0.1066
## 140 16.7343 nan 0.0100 0.1035
## 160 14.6880 nan 0.0100 0.1041
## 180 12.9930 nan 0.0100 0.0655
## 200 11.6174 nan 0.0100 0.0573
## 220 10.5061 nan 0.0100 0.0399
## 240 9.5584 nan 0.0100 0.0358
## 260 8.7304 nan 0.0100 0.0296
## 280 8.0585 nan 0.0100 0.0154
## 300 7.4698 nan 0.0100 0.0220
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.6210 nan 0.0100 0.7643
## 2 59.9189 nan 0.0100 0.7546
## 3 59.2374 nan 0.0100 0.7288
## 4 58.4934 nan 0.0100 0.7229
## 5 57.7318 nan 0.0100 0.7413
## 6 57.0263 nan 0.0100 0.7308
## 7 56.3427 nan 0.0100 0.6451
## 8 55.6939 nan 0.0100 0.7285
## 9 55.0414 nan 0.0100 0.6567
## 10 54.3798 nan 0.0100 0.6393
## 20 48.3319 nan 0.0100 0.5471
## 40 38.8705 nan 0.0100 0.3552
## 60 31.8643 nan 0.0100 0.2721
## 80 26.5174 nan 0.0100 0.2332
## 100 22.4726 nan 0.0100 0.1578
## 120 19.1952 nan 0.0100 0.1279
## 140 16.7255 nan 0.0100 0.0919
## 160 14.6642 nan 0.0100 0.0679
## 180 13.0349 nan 0.0100 0.0511
## 200 11.7320 nan 0.0100 0.0558
## 220 10.6177 nan 0.0100 0.0514
## 240 9.6862 nan 0.0100 0.0366
## 260 8.9201 nan 0.0100 0.0354
## 280 8.2260 nan 0.0100 0.0205
## 300 7.6659 nan 0.0100 0.0220
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.3664 nan 0.0100 0.9490
## 2 59.4029 nan 0.0100 0.9511
## 3 58.4293 nan 0.0100 0.9291
## 4 57.4619 nan 0.0100 1.0679
## 5 56.4938 nan 0.0100 0.9267
## 6 55.5583 nan 0.0100 0.8971
## 7 54.5650 nan 0.0100 1.0088
## 8 53.6148 nan 0.0100 0.7895
## 9 52.7555 nan 0.0100 0.8632
## 10 51.9086 nan 0.0100 0.7475
## 20 44.1231 nan 0.0100 0.6960
## 40 32.2861 nan 0.0100 0.4852
## 60 24.1975 nan 0.0100 0.3348
## 80 18.4346 nan 0.0100 0.2284
## 100 14.3806 nan 0.0100 0.1761
## 120 11.5370 nan 0.0100 0.1153
## 140 9.4902 nan 0.0100 0.0971
## 160 7.9619 nan 0.0100 0.0696
## 180 6.8200 nan 0.0100 0.0453
## 200 5.9702 nan 0.0100 0.0279
## 220 5.3070 nan 0.0100 0.0144
## 240 4.7880 nan 0.0100 0.0158
## 260 4.3930 nan 0.0100 0.0112
## 280 4.1102 nan 0.0100 0.0004
## 300 3.8631 nan 0.0100 0.0028
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.3441 nan 0.0100 1.0884
## 2 59.3898 nan 0.0100 0.9820
## 3 58.4082 nan 0.0100 0.8843
## 4 57.4675 nan 0.0100 0.9217
## 5 56.5249 nan 0.0100 0.8988
## 6 55.6565 nan 0.0100 0.8703
## 7 54.6882 nan 0.0100 1.0201
## 8 53.7920 nan 0.0100 0.9010
## 9 52.8560 nan 0.0100 0.8745
## 10 51.9757 nan 0.0100 0.8403
## 20 44.1865 nan 0.0100 0.7569
## 40 32.4272 nan 0.0100 0.5145
## 60 24.2446 nan 0.0100 0.3711
## 80 18.4656 nan 0.0100 0.2337
## 100 14.5886 nan 0.0100 0.1065
## 120 11.7090 nan 0.0100 0.1212
## 140 9.6358 nan 0.0100 0.0848
## 160 8.0676 nan 0.0100 0.0557
## 180 6.9297 nan 0.0100 0.0340
## 200 6.0792 nan 0.0100 0.0228
## 220 5.4162 nan 0.0100 0.0264
## 240 4.9316 nan 0.0100 0.0122
## 260 4.5482 nan 0.0100 0.0079
## 280 4.2537 nan 0.0100 0.0042
## 300 4.0187 nan 0.0100 0.0046
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.3500 nan 0.0100 1.0190
## 2 59.3603 nan 0.0100 0.9367
## 3 58.4078 nan 0.0100 0.8853
## 4 57.3704 nan 0.0100 0.9471
## 5 56.4431 nan 0.0100 0.9108
## 6 55.5651 nan 0.0100 0.8060
## 7 54.6937 nan 0.0100 0.9691
## 8 53.7700 nan 0.0100 0.9101
## 9 52.8925 nan 0.0100 0.8669
## 10 52.0657 nan 0.0100 0.8167
## 20 44.3176 nan 0.0100 0.7018
## 40 32.4732 nan 0.0100 0.4926
## 60 24.3577 nan 0.0100 0.3101
## 80 18.7292 nan 0.0100 0.2253
## 100 14.7540 nan 0.0100 0.1218
## 120 11.9618 nan 0.0100 0.1111
## 140 9.8608 nan 0.0100 0.0783
## 160 8.3147 nan 0.0100 0.0568
## 180 7.1793 nan 0.0100 0.0443
## 200 6.2955 nan 0.0100 0.0220
## 220 5.6613 nan 0.0100 0.0207
## 240 5.1533 nan 0.0100 0.0164
## 260 4.7771 nan 0.0100 0.0100
## 280 4.4948 nan 0.0100 0.0078
## 300 4.2677 nan 0.0100 0.0028
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.2489 nan 0.0100 1.1364
## 2 59.1998 nan 0.0100 1.0307
## 3 58.0793 nan 0.0100 1.1226
## 4 57.0762 nan 0.0100 0.8950
## 5 56.0592 nan 0.0100 1.0230
## 6 55.0295 nan 0.0100 1.0202
## 7 54.1311 nan 0.0100 0.9347
## 8 53.2610 nan 0.0100 0.8762
## 9 52.3056 nan 0.0100 0.8626
## 10 51.3837 nan 0.0100 0.8446
## 20 43.2125 nan 0.0100 0.7242
## 40 31.1719 nan 0.0100 0.4753
## 60 22.8254 nan 0.0100 0.2939
## 80 17.0122 nan 0.0100 0.2435
## 100 13.0349 nan 0.0100 0.1575
## 120 10.1533 nan 0.0100 0.1000
## 140 8.1657 nan 0.0100 0.0674
## 160 6.6953 nan 0.0100 0.0468
## 180 5.6460 nan 0.0100 0.0283
## 200 4.8689 nan 0.0100 0.0279
## 220 4.2930 nan 0.0100 0.0172
## 240 3.8906 nan 0.0100 0.0077
## 260 3.5652 nan 0.0100 0.0043
## 280 3.3217 nan 0.0100 0.0044
## 300 3.1260 nan 0.0100 0.0025
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.2968 nan 0.0100 1.1529
## 2 59.2411 nan 0.0100 1.0693
## 3 58.2234 nan 0.0100 0.9467
## 4 57.1841 nan 0.0100 0.9436
## 5 56.2098 nan 0.0100 0.9415
## 6 55.2144 nan 0.0100 0.9335
## 7 54.3086 nan 0.0100 0.9898
## 8 53.3484 nan 0.0100 0.9354
## 9 52.4844 nan 0.0100 0.9183
## 10 51.5769 nan 0.0100 0.9541
## 20 43.3390 nan 0.0100 0.7148
## 40 31.0169 nan 0.0100 0.5128
## 60 22.7752 nan 0.0100 0.3130
## 80 17.0462 nan 0.0100 0.2339
## 100 13.0226 nan 0.0100 0.1655
## 120 10.1888 nan 0.0100 0.0911
## 140 8.1973 nan 0.0100 0.0754
## 160 6.7405 nan 0.0100 0.0550
## 180 5.7111 nan 0.0100 0.0302
## 200 4.9774 nan 0.0100 0.0258
## 220 4.4524 nan 0.0100 0.0058
## 240 4.0628 nan 0.0100 0.0133
## 260 3.7658 nan 0.0100 0.0100
## 280 3.5400 nan 0.0100 0.0055
## 300 3.3572 nan 0.0100 -0.0011
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.3088 nan 0.0100 0.9468
## 2 59.2464 nan 0.0100 1.0670
## 3 58.2532 nan 0.0100 0.9555
## 4 57.2127 nan 0.0100 0.9789
## 5 56.2379 nan 0.0100 0.8822
## 6 55.2641 nan 0.0100 1.0158
## 7 54.3385 nan 0.0100 0.9156
## 8 53.4357 nan 0.0100 0.8923
## 9 52.5017 nan 0.0100 0.9272
## 10 51.6417 nan 0.0100 0.8443
## 20 43.5650 nan 0.0100 0.7546
## 40 31.5142 nan 0.0100 0.4929
## 60 23.1920 nan 0.0100 0.3233
## 80 17.4470 nan 0.0100 0.1912
## 100 13.4063 nan 0.0100 0.1669
## 120 10.5748 nan 0.0100 0.1018
## 140 8.5803 nan 0.0100 0.0700
## 160 7.1628 nan 0.0100 0.0424
## 180 6.1542 nan 0.0100 0.0324
## 200 5.4242 nan 0.0100 0.0271
## 220 4.8959 nan 0.0100 0.0172
## 240 4.5136 nan 0.0100 0.0071
## 260 4.2139 nan 0.0100 0.0060
## 280 3.9963 nan 0.0100 -0.0015
## 300 3.8197 nan 0.0100 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.7044 nan 0.0500 3.6068
## 2 54.4860 nan 0.0500 3.6038
## 3 51.4795 nan 0.0500 3.0725
## 4 48.2829 nan 0.0500 2.7477
## 5 45.6534 nan 0.0500 2.4187
## 6 43.2131 nan 0.0500 2.4606
## 7 41.1646 nan 0.0500 2.1205
## 8 38.8947 nan 0.0500 2.1821
## 9 36.9404 nan 0.0500 1.8426
## 10 35.1515 nan 0.0500 1.5676
## 20 22.1450 nan 0.0500 0.8063
## 40 11.6054 nan 0.0500 0.2089
## 60 7.5295 nan 0.0500 0.0846
## 80 5.5828 nan 0.0500 0.0200
## 100 4.7260 nan 0.0500 0.0165
## 120 4.3173 nan 0.0500 0.0070
## 140 4.0960 nan 0.0500 -0.0066
## 160 3.9644 nan 0.0500 -0.0041
## 180 3.8590 nan 0.0500 -0.0016
## 200 3.7709 nan 0.0500 -0.0088
## 220 3.7133 nan 0.0500 -0.0113
## 240 3.6451 nan 0.0500 -0.0130
## 260 3.5943 nan 0.0500 -0.0192
## 280 3.5529 nan 0.0500 -0.0192
## 300 3.5120 nan 0.0500 -0.0054
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.7122 nan 0.0500 3.5301
## 2 54.1298 nan 0.0500 3.4151
## 3 51.0843 nan 0.0500 3.1384
## 4 48.2779 nan 0.0500 2.8377
## 5 45.6377 nan 0.0500 2.5901
## 6 43.3251 nan 0.0500 2.3360
## 7 41.0335 nan 0.0500 2.3503
## 8 38.8456 nan 0.0500 2.0521
## 9 36.9219 nan 0.0500 1.8290
## 10 35.2080 nan 0.0500 1.6629
## 20 22.3268 nan 0.0500 0.9230
## 40 11.4470 nan 0.0500 0.2404
## 60 7.4390 nan 0.0500 0.1025
## 80 5.6278 nan 0.0500 0.0516
## 100 4.7574 nan 0.0500 0.0200
## 120 4.3662 nan 0.0500 -0.0082
## 140 4.1667 nan 0.0500 -0.0029
## 160 4.0477 nan 0.0500 0.0005
## 180 3.9309 nan 0.0500 -0.0052
## 200 3.8595 nan 0.0500 -0.0194
## 220 3.7828 nan 0.0500 -0.0028
## 240 3.7262 nan 0.0500 -0.0055
## 260 3.6650 nan 0.0500 -0.0101
## 280 3.6227 nan 0.0500 -0.0072
## 300 3.5902 nan 0.0500 -0.0080
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.5812 nan 0.0500 3.8763
## 2 54.3611 nan 0.0500 3.3412
## 3 51.1497 nan 0.0500 3.0675
## 4 48.3818 nan 0.0500 3.0605
## 5 45.7192 nan 0.0500 2.6437
## 6 43.3694 nan 0.0500 2.3816
## 7 40.9379 nan 0.0500 2.3437
## 8 38.8622 nan 0.0500 2.0155
## 9 36.8831 nan 0.0500 1.6641
## 10 35.2245 nan 0.0500 1.7334
## 20 22.5093 nan 0.0500 0.8259
## 40 11.6596 nan 0.0500 0.3277
## 60 7.6610 nan 0.0500 0.0858
## 80 5.8494 nan 0.0500 0.0685
## 100 4.9810 nan 0.0500 0.0095
## 120 4.6103 nan 0.0500 0.0128
## 140 4.4076 nan 0.0500 0.0105
## 160 4.2791 nan 0.0500 -0.0190
## 180 4.1837 nan 0.0500 -0.0064
## 200 4.0930 nan 0.0500 0.0001
## 220 4.0244 nan 0.0500 -0.0168
## 240 3.9636 nan 0.0500 -0.0074
## 260 3.8951 nan 0.0500 -0.0096
## 280 3.8323 nan 0.0500 -0.0074
## 300 3.7825 nan 0.0500 -0.0038
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.2194 nan 0.0500 4.6312
## 2 51.9347 nan 0.0500 4.9296
## 3 48.0273 nan 0.0500 3.7486
## 4 44.2328 nan 0.0500 4.1860
## 5 40.7202 nan 0.0500 3.5817
## 6 37.6699 nan 0.0500 3.2747
## 7 34.9171 nan 0.0500 2.4521
## 8 32.3180 nan 0.0500 2.6075
## 9 29.9564 nan 0.0500 2.0336
## 10 27.7736 nan 0.0500 2.2388
## 20 14.2743 nan 0.0500 0.7548
## 40 6.0720 nan 0.0500 0.1678
## 60 3.9432 nan 0.0500 0.0556
## 80 3.3035 nan 0.0500 -0.0064
## 100 2.9980 nan 0.0500 -0.0194
## 120 2.7428 nan 0.0500 -0.0093
## 140 2.5584 nan 0.0500 -0.0061
## 160 2.4108 nan 0.0500 -0.0224
## 180 2.2766 nan 0.0500 0.0003
## 200 2.1684 nan 0.0500 -0.0157
## 220 2.0407 nan 0.0500 -0.0073
## 240 1.9323 nan 0.0500 -0.0095
## 260 1.8425 nan 0.0500 -0.0126
## 280 1.7615 nan 0.0500 -0.0163
## 300 1.6895 nan 0.0500 -0.0048
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.3794 nan 0.0500 5.0040
## 2 51.8961 nan 0.0500 4.4267
## 3 47.6231 nan 0.0500 3.5971
## 4 43.7211 nan 0.0500 3.5671
## 5 40.1669 nan 0.0500 3.5921
## 6 37.2068 nan 0.0500 2.4737
## 7 34.2648 nan 0.0500 2.6141
## 8 31.5883 nan 0.0500 2.5376
## 9 29.3471 nan 0.0500 2.1549
## 10 27.1261 nan 0.0500 2.3840
## 20 14.1624 nan 0.0500 0.7217
## 40 5.9939 nan 0.0500 0.1748
## 60 4.0245 nan 0.0500 0.0138
## 80 3.4240 nan 0.0500 -0.0055
## 100 3.1602 nan 0.0500 0.0083
## 120 2.9570 nan 0.0500 -0.0177
## 140 2.8229 nan 0.0500 -0.0174
## 160 2.6943 nan 0.0500 -0.0105
## 180 2.5606 nan 0.0500 -0.0099
## 200 2.4451 nan 0.0500 -0.0275
## 220 2.3247 nan 0.0500 -0.0113
## 240 2.2328 nan 0.0500 -0.0154
## 260 2.1604 nan 0.0500 -0.0140
## 280 2.0837 nan 0.0500 -0.0073
## 300 1.9897 nan 0.0500 -0.0100
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.1751 nan 0.0500 4.7242
## 2 51.9385 nan 0.0500 4.5791
## 3 47.7678 nan 0.0500 4.2664
## 4 43.9528 nan 0.0500 4.2168
## 5 40.4708 nan 0.0500 3.6818
## 6 37.4278 nan 0.0500 2.9515
## 7 34.7079 nan 0.0500 2.3996
## 8 32.2776 nan 0.0500 2.4402
## 9 30.0835 nan 0.0500 1.9938
## 10 27.9190 nan 0.0500 2.0398
## 20 14.5739 nan 0.0500 0.8620
## 40 6.4316 nan 0.0500 0.1544
## 60 4.4438 nan 0.0500 0.0307
## 80 3.8964 nan 0.0500 -0.0230
## 100 3.5835 nan 0.0500 -0.0383
## 120 3.3612 nan 0.0500 -0.0109
## 140 3.1757 nan 0.0500 -0.0074
## 160 2.9966 nan 0.0500 -0.0189
## 180 2.8774 nan 0.0500 -0.0184
## 200 2.7498 nan 0.0500 -0.0100
## 220 2.6249 nan 0.0500 -0.0238
## 240 2.5440 nan 0.0500 -0.0091
## 260 2.4550 nan 0.0500 -0.0088
## 280 2.3690 nan 0.0500 -0.0134
## 300 2.2901 nan 0.0500 -0.0115
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.3137 nan 0.0500 5.1372
## 2 51.5330 nan 0.0500 4.5945
## 3 47.2745 nan 0.0500 3.9988
## 4 43.3392 nan 0.0500 4.2006
## 5 39.7695 nan 0.0500 3.4859
## 6 36.5980 nan 0.0500 2.9833
## 7 33.7490 nan 0.0500 2.7244
## 8 31.0272 nan 0.0500 2.4015
## 9 28.5711 nan 0.0500 2.3339
## 10 26.4512 nan 0.0500 2.4860
## 20 12.6775 nan 0.0500 0.8647
## 40 4.6457 nan 0.0500 0.1575
## 60 3.0133 nan 0.0500 -0.0072
## 80 2.4535 nan 0.0500 -0.0168
## 100 2.1399 nan 0.0500 -0.0072
## 120 1.8872 nan 0.0500 -0.0139
## 140 1.7178 nan 0.0500 -0.0146
## 160 1.5734 nan 0.0500 -0.0093
## 180 1.4478 nan 0.0500 -0.0035
## 200 1.3158 nan 0.0500 -0.0177
## 220 1.2101 nan 0.0500 -0.0173
## 240 1.1332 nan 0.0500 -0.0058
## 260 1.0541 nan 0.0500 -0.0068
## 280 0.9790 nan 0.0500 -0.0072
## 300 0.9100 nan 0.0500 -0.0064
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.0808 nan 0.0500 5.5010
## 2 51.2346 nan 0.0500 4.4403
## 3 46.8758 nan 0.0500 4.1584
## 4 42.9460 nan 0.0500 3.6832
## 5 39.3608 nan 0.0500 3.3673
## 6 36.1963 nan 0.0500 3.4394
## 7 33.3486 nan 0.0500 3.1439
## 8 30.8008 nan 0.0500 2.6522
## 9 28.3031 nan 0.0500 2.2201
## 10 26.1856 nan 0.0500 2.0856
## 20 12.9055 nan 0.0500 0.8579
## 40 5.0199 nan 0.0500 0.1273
## 60 3.3143 nan 0.0500 -0.0143
## 80 2.7825 nan 0.0500 -0.0033
## 100 2.4598 nan 0.0500 -0.0247
## 120 2.2223 nan 0.0500 -0.0133
## 140 2.0390 nan 0.0500 -0.0197
## 160 1.8793 nan 0.0500 -0.0246
## 180 1.7536 nan 0.0500 -0.0106
## 200 1.6556 nan 0.0500 -0.0060
## 220 1.5493 nan 0.0500 -0.0103
## 240 1.4502 nan 0.0500 -0.0178
## 260 1.3722 nan 0.0500 -0.0050
## 280 1.3022 nan 0.0500 -0.0065
## 300 1.2402 nan 0.0500 -0.0112
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.1240 nan 0.0500 4.9851
## 2 51.5271 nan 0.0500 4.7105
## 3 47.2978 nan 0.0500 3.9636
## 4 43.2374 nan 0.0500 3.9442
## 5 39.6017 nan 0.0500 3.4928
## 6 36.5024 nan 0.0500 2.7629
## 7 33.7784 nan 0.0500 2.6104
## 8 31.1471 nan 0.0500 2.8854
## 9 28.7282 nan 0.0500 2.4637
## 10 26.5387 nan 0.0500 2.0653
## 20 13.1396 nan 0.0500 0.7494
## 40 5.2301 nan 0.0500 0.0377
## 60 3.8169 nan 0.0500 0.0036
## 80 3.3373 nan 0.0500 -0.0175
## 100 3.0738 nan 0.0500 -0.0143
## 120 2.8389 nan 0.0500 -0.0086
## 140 2.6198 nan 0.0500 -0.0253
## 160 2.4581 nan 0.0500 -0.0117
## 180 2.3145 nan 0.0500 -0.0014
## 200 2.1866 nan 0.0500 -0.0103
## 220 2.0803 nan 0.0500 -0.0039
## 240 1.9756 nan 0.0500 -0.0105
## 260 1.8827 nan 0.0500 -0.0068
## 280 1.8105 nan 0.0500 -0.0106
## 300 1.7249 nan 0.0500 -0.0172
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.6591 nan 0.1000 6.7715
## 2 48.3010 nan 0.1000 5.7867
## 3 42.4891 nan 0.1000 5.9609
## 4 38.2250 nan 0.1000 3.8666
## 5 33.9413 nan 0.1000 3.7782
## 6 30.6817 nan 0.1000 3.3427
## 7 27.9472 nan 0.1000 2.6073
## 8 25.7814 nan 0.1000 1.9178
## 9 23.2969 nan 0.1000 2.3313
## 10 21.4272 nan 0.1000 1.2739
## 20 11.5102 nan 0.1000 0.5290
## 40 5.5209 nan 0.1000 0.0758
## 60 4.2664 nan 0.1000 -0.0266
## 80 3.8975 nan 0.1000 -0.0090
## 100 3.7673 nan 0.1000 -0.0295
## 120 3.6611 nan 0.1000 -0.0328
## 140 3.5743 nan 0.1000 -0.0148
## 160 3.4770 nan 0.1000 -0.0314
## 180 3.3889 nan 0.1000 0.0021
## 200 3.3110 nan 0.1000 0.0021
## 220 3.2260 nan 0.1000 -0.0147
## 240 3.1853 nan 0.1000 -0.0135
## 260 3.1164 nan 0.1000 -0.0132
## 280 3.0687 nan 0.1000 -0.0172
## 300 3.0184 nan 0.1000 -0.0042
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.0675 nan 0.1000 7.7076
## 2 47.5763 nan 0.1000 5.7988
## 3 42.2040 nan 0.1000 5.5087
## 4 37.9913 nan 0.1000 4.1518
## 5 34.5586 nan 0.1000 3.4527
## 6 31.3010 nan 0.1000 3.1918
## 7 28.3196 nan 0.1000 2.8274
## 8 25.8142 nan 0.1000 2.2632
## 9 23.7193 nan 0.1000 2.1536
## 10 21.6919 nan 0.1000 1.5309
## 20 11.3291 nan 0.1000 0.5633
## 40 5.7202 nan 0.1000 0.1444
## 60 4.3753 nan 0.1000 0.0114
## 80 4.0448 nan 0.1000 -0.0121
## 100 3.8701 nan 0.1000 -0.0063
## 120 3.7362 nan 0.1000 -0.0180
## 140 3.6115 nan 0.1000 -0.0079
## 160 3.5389 nan 0.1000 -0.0043
## 180 3.4667 nan 0.1000 -0.0047
## 200 3.3945 nan 0.1000 -0.0409
## 220 3.3553 nan 0.1000 -0.0122
## 240 3.3044 nan 0.1000 -0.0302
## 260 3.2371 nan 0.1000 -0.0126
## 280 3.1889 nan 0.1000 -0.0074
## 300 3.1440 nan 0.1000 -0.0174
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.0901 nan 0.1000 7.5111
## 2 47.7363 nan 0.1000 5.7829
## 3 42.5440 nan 0.1000 4.8537
## 4 38.7286 nan 0.1000 3.9717
## 5 34.9872 nan 0.1000 3.2201
## 6 31.5318 nan 0.1000 3.1716
## 7 28.2819 nan 0.1000 2.8026
## 8 25.9236 nan 0.1000 2.4033
## 9 23.6226 nan 0.1000 2.1979
## 10 22.0039 nan 0.1000 1.4336
## 20 11.7075 nan 0.1000 0.3756
## 40 6.0787 nan 0.1000 0.0725
## 60 4.7798 nan 0.1000 -0.0153
## 80 4.4986 nan 0.1000 -0.0529
## 100 4.2960 nan 0.1000 -0.0206
## 120 4.1212 nan 0.1000 0.0028
## 140 3.9919 nan 0.1000 -0.0649
## 160 3.8405 nan 0.1000 -0.0372
## 180 3.7531 nan 0.1000 -0.0065
## 200 3.6914 nan 0.1000 -0.0238
## 220 3.6297 nan 0.1000 -0.0080
## 240 3.5573 nan 0.1000 -0.0091
## 260 3.5090 nan 0.1000 -0.0045
## 280 3.4254 nan 0.1000 -0.0096
## 300 3.3711 nan 0.1000 -0.0162
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.6544 nan 0.1000 10.0984
## 2 43.7615 nan 0.1000 6.5339
## 3 36.8242 nan 0.1000 6.1128
## 4 31.6833 nan 0.1000 5.6614
## 5 27.0822 nan 0.1000 4.5967
## 6 23.7313 nan 0.1000 3.4297
## 7 20.8276 nan 0.1000 2.7725
## 8 18.2024 nan 0.1000 2.6371
## 9 16.2313 nan 0.1000 2.1914
## 10 14.2079 nan 0.1000 1.5638
## 20 5.8131 nan 0.1000 0.2346
## 40 3.3491 nan 0.1000 -0.0282
## 60 2.7920 nan 0.1000 -0.0128
## 80 2.4312 nan 0.1000 -0.0156
## 100 2.1622 nan 0.1000 -0.0418
## 120 1.9378 nan 0.1000 -0.0372
## 140 1.7871 nan 0.1000 -0.0262
## 160 1.6445 nan 0.1000 -0.0153
## 180 1.5066 nan 0.1000 -0.0165
## 200 1.3807 nan 0.1000 -0.0146
## 220 1.2761 nan 0.1000 -0.0124
## 240 1.1967 nan 0.1000 -0.0102
## 260 1.1261 nan 0.1000 -0.0059
## 280 1.0537 nan 0.1000 -0.0243
## 300 0.9882 nan 0.1000 -0.0068
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.4786 nan 0.1000 9.7103
## 2 43.5594 nan 0.1000 8.0911
## 3 36.9666 nan 0.1000 6.6066
## 4 31.9142 nan 0.1000 6.0019
## 5 27.5164 nan 0.1000 4.0046
## 6 23.7718 nan 0.1000 3.4984
## 7 20.7906 nan 0.1000 2.8973
## 8 18.1682 nan 0.1000 2.4754
## 9 16.2047 nan 0.1000 1.8840
## 10 14.1921 nan 0.1000 1.8087
## 20 6.1461 nan 0.1000 0.3012
## 40 3.4691 nan 0.1000 -0.0183
## 60 2.9709 nan 0.1000 -0.0594
## 80 2.6037 nan 0.1000 -0.0039
## 100 2.3855 nan 0.1000 -0.0399
## 120 2.1780 nan 0.1000 -0.0203
## 140 1.9992 nan 0.1000 -0.0121
## 160 1.8866 nan 0.1000 -0.0140
## 180 1.7876 nan 0.1000 -0.0352
## 200 1.6674 nan 0.1000 -0.0230
## 220 1.5755 nan 0.1000 -0.0183
## 240 1.4904 nan 0.1000 -0.0258
## 260 1.4021 nan 0.1000 -0.0199
## 280 1.3356 nan 0.1000 -0.0088
## 300 1.2642 nan 0.1000 -0.0169
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.7884 nan 0.1000 9.6041
## 2 43.5082 nan 0.1000 7.7349
## 3 36.7723 nan 0.1000 6.6486
## 4 31.2501 nan 0.1000 5.0619
## 5 27.4044 nan 0.1000 4.4218
## 6 23.8806 nan 0.1000 3.6108
## 7 20.8837 nan 0.1000 2.8841
## 8 18.4225 nan 0.1000 1.9319
## 9 16.4330 nan 0.1000 1.5314
## 10 14.6498 nan 0.1000 1.7993
## 20 6.1358 nan 0.1000 0.2728
## 40 3.7223 nan 0.1000 -0.0273
## 60 3.2456 nan 0.1000 -0.0638
## 80 2.9398 nan 0.1000 -0.0105
## 100 2.7081 nan 0.1000 -0.0211
## 120 2.5264 nan 0.1000 -0.0281
## 140 2.3295 nan 0.1000 -0.0573
## 160 2.1631 nan 0.1000 -0.0171
## 180 2.0247 nan 0.1000 -0.0099
## 200 1.9037 nan 0.1000 -0.0195
## 220 1.8217 nan 0.1000 -0.0149
## 240 1.7207 nan 0.1000 -0.0115
## 260 1.6548 nan 0.1000 -0.0428
## 280 1.5657 nan 0.1000 -0.0149
## 300 1.4950 nan 0.1000 -0.0078
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.9588 nan 0.1000 9.4794
## 2 42.5804 nan 0.1000 8.4162
## 3 35.7943 nan 0.1000 6.1026
## 4 30.1986 nan 0.1000 5.0705
## 5 25.4983 nan 0.1000 4.6203
## 6 21.8833 nan 0.1000 3.3288
## 7 18.8696 nan 0.1000 2.6876
## 8 16.1460 nan 0.1000 2.3235
## 9 14.1126 nan 0.1000 1.9322
## 10 12.3574 nan 0.1000 1.5953
## 20 4.7097 nan 0.1000 0.2904
## 40 2.4959 nan 0.1000 -0.0006
## 60 1.9685 nan 0.1000 -0.0520
## 80 1.6370 nan 0.1000 -0.0200
## 100 1.3850 nan 0.1000 -0.0268
## 120 1.1901 nan 0.1000 -0.0074
## 140 1.0546 nan 0.1000 -0.0182
## 160 0.9172 nan 0.1000 -0.0157
## 180 0.7932 nan 0.1000 -0.0106
## 200 0.7076 nan 0.1000 -0.0139
## 220 0.6218 nan 0.1000 -0.0172
## 240 0.5482 nan 0.1000 -0.0112
## 260 0.4917 nan 0.1000 -0.0086
## 280 0.4423 nan 0.1000 -0.0106
## 300 0.4010 nan 0.1000 -0.0053
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.0314 nan 0.1000 10.6135
## 2 42.7274 nan 0.1000 7.7280
## 3 35.9765 nan 0.1000 7.4152
## 4 30.3635 nan 0.1000 5.1352
## 5 25.7286 nan 0.1000 4.1265
## 6 21.8343 nan 0.1000 4.0708
## 7 18.6432 nan 0.1000 3.0269
## 8 16.0085 nan 0.1000 2.4161
## 9 13.8918 nan 0.1000 2.1323
## 10 12.1956 nan 0.1000 1.5724
## 20 4.7421 nan 0.1000 0.2029
## 40 2.7459 nan 0.1000 -0.0239
## 60 2.2100 nan 0.1000 -0.0393
## 80 1.9041 nan 0.1000 -0.0152
## 100 1.6773 nan 0.1000 -0.0312
## 120 1.4970 nan 0.1000 -0.0218
## 140 1.3284 nan 0.1000 -0.0142
## 160 1.1809 nan 0.1000 -0.0247
## 180 1.0333 nan 0.1000 -0.0112
## 200 0.9402 nan 0.1000 -0.0063
## 220 0.8640 nan 0.1000 -0.0118
## 240 0.7870 nan 0.1000 -0.0175
## 260 0.7268 nan 0.1000 -0.0274
## 280 0.6661 nan 0.1000 -0.0077
## 300 0.6106 nan 0.1000 -0.0092
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.3636 nan 0.1000 10.1561
## 2 42.7844 nan 0.1000 7.0321
## 3 35.9204 nan 0.1000 6.8687
## 4 30.4760 nan 0.1000 5.3386
## 5 25.7371 nan 0.1000 4.2528
## 6 22.0112 nan 0.1000 3.7777
## 7 18.9485 nan 0.1000 3.0504
## 8 16.4187 nan 0.1000 2.3605
## 9 14.3610 nan 0.1000 1.8215
## 10 12.7530 nan 0.1000 1.6211
## 20 5.2737 nan 0.1000 0.1733
## 40 3.3011 nan 0.1000 -0.0077
## 60 2.8406 nan 0.1000 -0.0487
## 80 2.4461 nan 0.1000 -0.0250
## 100 2.1746 nan 0.1000 -0.0229
## 120 1.9425 nan 0.1000 -0.0073
## 140 1.7949 nan 0.1000 -0.0192
## 160 1.6444 nan 0.1000 -0.0213
## 180 1.5138 nan 0.1000 -0.0053
## 200 1.4078 nan 0.1000 -0.0071
## 220 1.2648 nan 0.1000 -0.0123
## 240 1.1639 nan 0.1000 -0.0140
## 260 1.0858 nan 0.1000 -0.0092
## 280 1.0228 nan 0.1000 -0.0249
## 300 0.9577 nan 0.1000 -0.0125
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.4616 nan 0.1000 10.3905
## 2 44.3583 nan 0.1000 8.1397
## 3 37.4620 nan 0.1000 6.1114
## 4 31.8007 nan 0.1000 5.6530
## 5 27.0376 nan 0.1000 4.5885
## 6 23.3893 nan 0.1000 3.3185
## 7 20.4557 nan 0.1000 2.7501
## 8 17.7660 nan 0.1000 2.5441
## 9 15.7836 nan 0.1000 2.0778
## 10 14.0228 nan 0.1000 1.7046
## 20 5.9281 nan 0.1000 0.2932
## 40 3.4548 nan 0.1000 -0.0049
## 60 2.9358 nan 0.1000 -0.0250
## 80 2.6003 nan 0.1000 -0.0225
## 100 2.3949 nan 0.1000 -0.0240
## 120 2.1777 nan 0.1000 -0.0255
## 140 2.0366 nan 0.1000 -0.0287
## 160 1.8911 nan 0.1000 -0.0440
## 180 1.7667 nan 0.1000 -0.0167
## 200 1.6584 nan 0.1000 -0.0249
##################################
# Reporting the apparent results
# for the GBM model
##################################
GBM_DALEX <- DALEX::explain(GBM_Tune,
data = MD.Model.Predictors,
y = MD$LIFEXP,
verbose = FALSE,
label = "GBM")
(GBM_DALEX_Performance <- model_performance(GBM_DALEX))## Measures for: regression
## mse : 1.658418
## rmse : 1.287796
## r2 : 0.9732008
## mad : 0.7400697
##
## Residuals:
## 0% 10% 20% 30% 40% 50%
## -4.73887723 -1.34111721 -0.83535168 -0.49339743 -0.22544393 -0.05181421
## 60% 70% 80% 90% 100%
## 0.16238009 0.52825030 0.91403922 1.67095243 4.33309954
(GBM_DALEX_Diagnostics <- model_diagnostics(GBM_DALEX))## GENDER CONTIN INFMOR PERCAP
## Male :139 Africa :89 Min. :0.3365 Min. :-1.4775
## Female:153 Asia :73 1st Qu.:1.7047 1st Qu.: 0.6183
## Europe :62 Median :2.6100 Median : 1.7960
## North America:31 Mean :2.5569 Mean : 1.7571
## Oceania :18 3rd Qu.:3.5025 3rd Qu.: 2.7938
## South America:19 Max. :4.4864 Max. : 4.7293
## CLTECH NCOMOR y y_hat
## Min. : 0.00 Min. :1.866 Min. :52.84 Min. :55.18
## 1st Qu.: 24.35 1st Qu.:3.944 1st Qu.:66.93 1st Qu.:66.96
## Median : 82.80 Median :4.717 Median :73.53 Median :73.23
## Mean : 64.60 Mean :4.652 Mean :72.47 Mean :72.45
## 3rd Qu.:100.00 3rd Qu.:5.347 3rd Qu.:78.54 3rd Qu.:78.71
## Max. :100.00 Max. :7.959 Max. :87.45 Max. :84.94
## residuals abs_residuals label ids
## Min. :-4.73888 Min. :0.00415 Length:292 Min. : 1.00
## 1st Qu.:-0.70603 1st Qu.:0.27696 Class :character 1st Qu.: 73.75
## Median :-0.05181 Median :0.74007 Mode :character Median :146.50
## Mean : 0.02483 Mean :0.94179 Mean :146.50
## 3rd Qu.: 0.75568 3rd Qu.:1.29372 3rd Qu.:219.25
## Max. : 4.33310 Max. :4.73888 Max. :292.00
plot(GBM_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("GBM: Observed and Predicted LIFEXP")GBM_DALEX_VariableImportance <- model_parts(GBM_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
plot(GBM_DALEX_VariableImportance)##################################
# Reporting the cross-validation results
# for the GBM model
##################################
GBM_Tune## Stochastic Gradient Boosting
##
## 292 samples
## 6 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 264, 263, 263, 262, 263, 264, ...
## Resampling results across tuning parameters:
##
## shrinkage interaction.depth n.minobsinnode n.trees RMSE Rsquared
## 0.01 1 5 100 4.772676 0.8333691
## 0.01 1 5 200 3.515429 0.8726404
## 0.01 1 5 300 2.877624 0.8964547
## 0.01 1 10 100 4.793621 0.8332069
## 0.01 1 10 200 3.521911 0.8733517
## 0.01 1 10 300 2.890117 0.8956765
## 0.01 1 15 100 4.793589 0.8339653
## 0.01 1 15 200 3.530219 0.8713678
## 0.01 1 15 300 2.904276 0.8956071
## 0.01 3 5 100 3.892040 0.8947209
## 0.01 3 5 200 2.654034 0.9139912
## 0.01 3 5 300 2.264173 0.9235081
## 0.01 3 10 100 3.896555 0.8970888
## 0.01 3 10 200 2.650019 0.9149851
## 0.01 3 10 300 2.270424 0.9234616
## 0.01 3 15 100 3.936460 0.8916345
## 0.01 3 15 200 2.697619 0.9125729
## 0.01 3 15 300 2.311237 0.9215290
## 0.01 5 5 100 3.698565 0.9106552
## 0.01 5 5 200 2.476083 0.9228052
## 0.01 5 5 300 2.189965 0.9274235
## 0.01 5 10 100 3.694360 0.9124533
## 0.01 5 10 200 2.483889 0.9223152
## 0.01 5 10 300 2.188089 0.9272777
## 0.01 5 15 100 3.743395 0.9073428
## 0.01 5 15 200 2.537516 0.9193448
## 0.01 5 15 300 2.252018 0.9238906
## 0.05 1 5 100 2.348301 0.9165106
## 0.05 1 5 200 2.164617 0.9255460
## 0.05 1 5 300 2.145105 0.9272272
## 0.05 1 10 100 2.371539 0.9151106
## 0.05 1 10 200 2.192438 0.9241736
## 0.05 1 10 300 2.175619 0.9254071
## 0.05 1 15 100 2.418639 0.9118379
## 0.05 1 15 200 2.246551 0.9215537
## 0.05 1 15 300 2.210928 0.9234890
## 0.05 3 5 100 2.132971 0.9279468
## 0.05 3 5 200 2.128585 0.9281739
## 0.05 3 5 300 2.138451 0.9271950
## 0.05 3 10 100 2.151650 0.9270914
## 0.05 3 10 200 2.134122 0.9280313
## 0.05 3 10 300 2.140481 0.9273580
## 0.05 3 15 100 2.210260 0.9236464
## 0.05 3 15 200 2.175687 0.9253416
## 0.05 3 15 300 2.171212 0.9250317
## 0.05 5 5 100 2.183111 0.9251933
## 0.05 5 5 200 2.197547 0.9232986
## 0.05 5 5 300 2.192592 0.9235604
## 0.05 5 10 100 2.125501 0.9289397
## 0.05 5 10 200 2.148236 0.9267642
## 0.05 5 10 300 2.149715 0.9262640
## 0.05 5 15 100 2.160418 0.9266001
## 0.05 5 15 200 2.173699 0.9253182
## 0.05 5 15 300 2.169584 0.9248442
## 0.10 1 5 100 2.216683 0.9224492
## 0.10 1 5 200 2.171832 0.9249045
## 0.10 1 5 300 2.163527 0.9254722
## 0.10 1 10 100 2.190542 0.9240748
## 0.10 1 10 200 2.135899 0.9276319
## 0.10 1 10 300 2.132410 0.9273953
## 0.10 1 15 100 2.303119 0.9177669
## 0.10 1 15 200 2.211956 0.9236257
## 0.10 1 15 300 2.191344 0.9244899
## 0.10 3 5 100 2.165442 0.9258963
## 0.10 3 5 200 2.157851 0.9261405
## 0.10 3 5 300 2.165798 0.9247466
## 0.10 3 10 100 2.131881 0.9280289
## 0.10 3 10 200 2.107162 0.9285203
## 0.10 3 10 300 2.135974 0.9258930
## 0.10 3 15 100 2.169556 0.9263259
## 0.10 3 15 200 2.161955 0.9250116
## 0.10 3 15 300 2.145423 0.9254143
## 0.10 5 5 100 2.193752 0.9240341
## 0.10 5 5 200 2.214680 0.9221060
## 0.10 5 5 300 2.218715 0.9214655
## 0.10 5 10 100 2.212554 0.9219448
## 0.10 5 10 200 2.239859 0.9195548
## 0.10 5 10 300 2.261049 0.9178753
## 0.10 5 15 100 2.189393 0.9242153
## 0.10 5 15 200 2.205440 0.9225160
## 0.10 5 15 300 2.194315 0.9223180
## MAE
## 3.858956
## 2.840899
## 2.288121
## 3.875879
## 2.845201
## 2.289532
## 3.869335
## 2.827283
## 2.279083
## 3.222475
## 2.104761
## 1.721413
## 3.232783
## 2.091787
## 1.710329
## 3.246277
## 2.109478
## 1.728179
## 3.068383
## 1.947763
## 1.654183
## 3.060625
## 1.945668
## 1.626496
## 3.087076
## 1.962356
## 1.666117
## 1.798163
## 1.599649
## 1.579040
## 1.802411
## 1.633652
## 1.615435
## 1.815861
## 1.661022
## 1.629704
## 1.580361
## 1.572619
## 1.582000
## 1.592463
## 1.563340
## 1.574442
## 1.624147
## 1.596711
## 1.587831
## 1.609724
## 1.604475
## 1.612752
## 1.566812
## 1.581413
## 1.586848
## 1.574829
## 1.585161
## 1.574231
## 1.644726
## 1.591006
## 1.584273
## 1.610851
## 1.562029
## 1.575392
## 1.708563
## 1.627455
## 1.610661
## 1.593192
## 1.608458
## 1.631719
## 1.570714
## 1.559041
## 1.593043
## 1.575222
## 1.593423
## 1.573554
## 1.617333
## 1.630894
## 1.638725
## 1.617015
## 1.634059
## 1.649568
## 1.616787
## 1.619968
## 1.617048
##
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were n.trees = 200, interaction.depth =
## 3, shrinkage = 0.1 and n.minobsinnode = 10.
GBM_Tune$finalModel## A gradient boosted model with gaussian loss function.
## 200 iterations were performed.
## There were 6 predictors of which 6 had non-zero influence.
(GBM_Tune_RMSE <- GBM_Tune$results[GBM_Tune$results$shrinkage==GBM_Tune$bestTune$shrinkage &
GBM_Tune$results$interaction.depth==GBM_Tune$bestTune$interaction.depth &
GBM_Tune$results$n.minobsinnode==GBM_Tune$bestTune$n.minobsinnode &
GBM_Tune$results$n.trees==GBM_Tune$bestTune$n.trees,
c("RMSE")])## [1] 2.107162
(GBM_Tune_Rsquared <- GBM_Tune$results[GBM_Tune$results$shrinkage==GBM_Tune$bestTune$shrinkage &
GBM_Tune$results$interaction.depth==GBM_Tune$bestTune$interaction.depth &
GBM_Tune$results$n.minobsinnode==GBM_Tune$bestTune$n.minobsinnode &
GBM_Tune$results$n.trees==GBM_Tune$bestTune$n.trees,
c("Rsquared")])## [1] 0.9285203
(GBM_Tune_MAE <- GBM_Tune$results[GBM_Tune$results$shrinkage==GBM_Tune$bestTune$shrinkage &
GBM_Tune$results$interaction.depth==GBM_Tune$bestTune$interaction.depth &
GBM_Tune$results$n.minobsinnode==GBM_Tune$bestTune$n.minobsinnode &
GBM_Tune$results$n.trees==GBM_Tune$bestTune$n.trees,
c("MAE")])## [1] 1.559041
##################################
# Defining the model hyperparameter values
# for the RF model
##################################
RF_Grid = data.frame(mtry = c(100, 200, 300, 400, 500,
600, 700, 800, 900, 1000))
##################################
# Running the RF model
# by setting the caret method to 'RF'
##################################
set.seed(12345678)
RF_Tune <- train(x = MD.Model.Predictors,
y = MD$LIFEXP,
method = "rf",
tuneGrid = RF_Grid,
trControl = KFold_Control)
##################################
# Reporting the apparent results
# for the RF model
##################################
RF_DALEX <- DALEX::explain(RF_Tune,
data = MD.Model.Predictors,
y = MD$LIFEXP,
verbose = FALSE,
label = "RF")
(RF_DALEX_Performance <- model_performance(RF_DALEX))## Measures for: regression
## mse : 0.9404626
## rmse : 0.9697745
## r2 : 0.9848026
## mad : 0.493929
##
## Residuals:
## 0% 10% 20% 30% 40% 50%
## -3.89311943 -1.04739946 -0.63797813 -0.37774333 -0.17489179 -0.02355452
## 60% 70% 80% 90% 100%
## 0.11686047 0.37368288 0.59428965 1.20572149 3.83678360
(RF_DALEX_Diagnostics <- model_diagnostics(RF_DALEX))## GENDER CONTIN INFMOR PERCAP
## Male :139 Africa :89 Min. :0.3365 Min. :-1.4775
## Female:153 Asia :73 1st Qu.:1.7047 1st Qu.: 0.6183
## Europe :62 Median :2.6100 Median : 1.7960
## North America:31 Mean :2.5569 Mean : 1.7571
## Oceania :18 3rd Qu.:3.5025 3rd Qu.: 2.7938
## South America:19 Max. :4.4864 Max. : 4.7293
## CLTECH NCOMOR y y_hat
## Min. : 0.00 Min. :1.866 Min. :52.84 Min. :54.37
## 1st Qu.: 24.35 1st Qu.:3.944 1st Qu.:66.93 1st Qu.:67.15
## Median : 82.80 Median :4.717 Median :73.53 Median :73.58
## Mean : 64.60 Mean :4.652 Mean :72.47 Mean :72.49
## 3rd Qu.:100.00 3rd Qu.:5.347 3rd Qu.:78.54 3rd Qu.:78.49
## Max. :100.00 Max. :7.959 Max. :87.45 Max. :86.25
## residuals abs_residuals label ids
## Min. :-3.89312 Min. :0.004567 Length:292 Min. : 1.00
## 1st Qu.:-0.49373 1st Qu.:0.228644 Class :character 1st Qu.: 73.75
## Median :-0.02355 Median :0.493929 Mode :character Median :146.50
## Mean :-0.01104 Mean :0.695743 Mean :146.50
## 3rd Qu.: 0.49410 3rd Qu.:0.996858 3rd Qu.:219.25
## Max. : 3.83678 Max. :3.893119 Max. :292.00
plot(RF_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("RF: Observed and Predicted LIFEXP")RF_DALEX_VariableImportance <- model_parts(RF_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
plot(RF_DALEX_VariableImportance)##################################
# Reporting the cross-validation results
# for the RF model
##################################
RF_Tune## Random Forest
##
## 292 samples
## 6 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 264, 263, 263, 262, 263, 264, ...
## Resampling results across tuning parameters:
##
## mtry RMSE Rsquared MAE
## 100 2.244547 0.9196737 1.619943
## 200 2.249565 0.9196008 1.620459
## 300 2.251214 0.9194065 1.628754
## 400 2.239011 0.9201778 1.618476
## 500 2.244594 0.9199741 1.623557
## 600 2.250218 0.9195438 1.627748
## 700 2.250948 0.9195926 1.627966
## 800 2.252804 0.9194439 1.626298
## 900 2.256902 0.9191818 1.630857
## 1000 2.245525 0.9198362 1.618343
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was mtry = 400.
RF_Tune$finalModel##
## Call:
## randomForest(x = x, y = y, mtry = param$mtry)
## Type of random forest: regression
## Number of trees: 500
## No. of variables tried at each split: 6
##
## Mean of squared residuals: 5.126832
## % Var explained: 91.72
(RF_Tune_RMSE <- RF_Tune$results[RF_Tune$results$mtry==RF_Tune$bestTune$mtry,
c("RMSE")])## [1] 2.239011
(RF_Tune_Rsquared <- RF_Tune$results[RF_Tune$results$mtry==RF_Tune$bestTune$mtry,
c("Rsquared")])## [1] 0.9201778
(RF_Tune_MAE <- RF_Tune$results[RF_Tune$results$mtry==RF_Tune$bestTune$mtry,
c("MAE")])## [1] 1.618476
##################################
# Defining the model hyperparameter values
# for the NN model
##################################
NN_Grid = expand.grid(size = c(2, 5, 10, 15, 20),
decay = c(0, 0.1, 0.001, 0.0001, 0.00001))
##################################
# Running the NN model
# by setting the caret method to 'NN'
##################################
set.seed(12345678)
NN_Tune <- train(x = MD.Model.Predictors,
y = MD$LIFEXP,
method = "nnet",
linout = TRUE,
preProcess = c('center', 'scale'),
maxit = 500,
tuneGrid = NN_Grid,
trControl = KFold_Control)## # weights: 25
## initial value 1400339.778929
## iter 10 value 68496.910319
## iter 20 value 5956.174709
## iter 30 value 5844.262957
## iter 40 value 5352.739998
## iter 50 value 5259.580299
## iter 60 value 5258.019066
## iter 70 value 5205.090145
## iter 80 value 5196.195489
## iter 90 value 4833.831031
## iter 100 value 4569.510587
## iter 110 value 4553.721628
## iter 120 value 4553.045122
## iter 130 value 4553.015144
## iter 140 value 4552.876106
## iter 150 value 4550.489828
## iter 160 value 4550.208091
## iter 170 value 4550.152984
## iter 180 value 4550.031282
## iter 190 value 4549.764089
## iter 200 value 4549.444274
## iter 210 value 4548.348059
## iter 220 value 4548.157434
## iter 220 value 4548.157430
## iter 220 value 4548.157430
## final value 4548.157430
## converged
## # weights: 61
## initial value 1419402.900363
## iter 10 value 5466.160778
## iter 20 value 4136.215442
## iter 30 value 3233.377310
## iter 40 value 2184.748970
## iter 50 value 1482.694097
## iter 60 value 1341.742361
## iter 70 value 1297.232211
## iter 80 value 1213.738131
## iter 90 value 1146.376381
## iter 100 value 1070.241735
## iter 110 value 1030.232654
## iter 120 value 1015.217766
## iter 130 value 1005.639574
## iter 140 value 998.773821
## iter 150 value 992.324116
## iter 160 value 980.531489
## iter 170 value 978.495782
## iter 180 value 978.409744
## iter 190 value 978.361243
## iter 200 value 978.318025
## iter 210 value 978.296926
## iter 220 value 968.807011
## iter 230 value 939.853517
## iter 240 value 925.037978
## iter 250 value 919.646061
## iter 260 value 919.163802
## iter 270 value 917.547708
## iter 280 value 914.888136
## iter 290 value 911.397634
## iter 300 value 909.574590
## iter 310 value 908.742002
## iter 320 value 908.217371
## iter 330 value 907.307508
## iter 340 value 906.728904
## iter 350 value 906.413133
## iter 360 value 906.376723
## iter 370 value 906.329715
## iter 380 value 906.279582
## iter 390 value 906.271020
## iter 400 value 906.268970
## final value 906.268949
## converged
## # weights: 121
## initial value 1369973.913442
## iter 10 value 1456.280282
## iter 20 value 981.019262
## iter 30 value 820.891644
## iter 40 value 691.064010
## iter 50 value 615.847093
## iter 60 value 552.618476
## iter 70 value 501.933326
## iter 80 value 465.287566
## iter 90 value 435.639678
## iter 100 value 411.632837
## iter 110 value 377.319124
## iter 120 value 355.295277
## iter 130 value 332.770009
## iter 140 value 319.998174
## iter 150 value 311.670112
## iter 160 value 296.995967
## iter 170 value 289.673758
## iter 180 value 286.817395
## iter 190 value 285.327350
## iter 200 value 283.822117
## iter 210 value 281.823769
## iter 220 value 280.604183
## iter 230 value 279.960985
## iter 240 value 279.298747
## iter 250 value 278.767676
## iter 260 value 278.387227
## iter 270 value 277.570900
## iter 280 value 276.221841
## iter 290 value 273.862125
## iter 300 value 271.110331
## iter 310 value 269.686949
## iter 320 value 268.329167
## iter 330 value 266.353429
## iter 340 value 263.244321
## iter 350 value 262.025670
## iter 360 value 260.468974
## iter 370 value 259.082188
## iter 380 value 258.024935
## iter 390 value 257.709959
## iter 400 value 257.192116
## iter 410 value 256.946798
## iter 420 value 256.805007
## iter 430 value 256.770626
## iter 440 value 256.766595
## iter 450 value 256.763944
## iter 460 value 256.762805
## iter 470 value 256.759951
## iter 480 value 256.742706
## final value 256.731685
## converged
## # weights: 181
## initial value 1369859.779729
## iter 10 value 1218.791825
## iter 20 value 844.152299
## iter 30 value 679.366662
## iter 40 value 572.273305
## iter 50 value 466.657882
## iter 60 value 383.138573
## iter 70 value 339.951431
## iter 80 value 283.173217
## iter 90 value 257.527205
## iter 100 value 237.333601
## iter 110 value 223.739667
## iter 120 value 215.169024
## iter 130 value 208.082918
## iter 140 value 199.329810
## iter 150 value 191.342838
## iter 160 value 184.771082
## iter 170 value 174.014610
## iter 180 value 167.789067
## iter 190 value 161.398029
## iter 200 value 156.888476
## iter 210 value 153.435980
## iter 220 value 149.579761
## iter 230 value 146.046728
## iter 240 value 142.808104
## iter 250 value 140.214188
## iter 260 value 137.517961
## iter 270 value 135.175708
## iter 280 value 132.482019
## iter 290 value 129.935560
## iter 300 value 127.301305
## iter 310 value 124.632966
## iter 320 value 122.671009
## iter 330 value 119.063976
## iter 340 value 115.950398
## iter 350 value 113.321558
## iter 360 value 110.553146
## iter 370 value 109.537855
## iter 380 value 108.980326
## iter 390 value 108.142979
## iter 400 value 107.033356
## iter 410 value 106.482910
## iter 420 value 105.642504
## iter 430 value 104.684540
## iter 440 value 103.914639
## iter 450 value 102.861627
## iter 460 value 101.598500
## iter 470 value 99.744133
## iter 480 value 97.351172
## iter 490 value 95.563516
## iter 500 value 93.616614
## final value 93.616614
## stopped after 500 iterations
## # weights: 241
## initial value 1362136.072048
## iter 10 value 1398.158403
## iter 20 value 813.986040
## iter 30 value 623.314450
## iter 40 value 451.572085
## iter 50 value 343.423349
## iter 60 value 299.481028
## iter 70 value 258.539324
## iter 80 value 218.722855
## iter 90 value 189.014309
## iter 100 value 172.421623
## iter 110 value 152.085135
## iter 120 value 140.177393
## iter 130 value 130.443034
## iter 140 value 121.601687
## iter 150 value 114.523579
## iter 160 value 107.718749
## iter 170 value 97.564388
## iter 180 value 92.637160
## iter 190 value 87.632255
## iter 200 value 83.334630
## iter 210 value 78.936737
## iter 220 value 74.404872
## iter 230 value 71.245308
## iter 240 value 69.350806
## iter 250 value 66.556843
## iter 260 value 63.476653
## iter 270 value 61.090865
## iter 280 value 59.602674
## iter 290 value 58.022193
## iter 300 value 55.931747
## iter 310 value 54.279973
## iter 320 value 52.788004
## iter 330 value 52.144060
## iter 340 value 51.520501
## iter 350 value 50.937822
## iter 360 value 50.600355
## iter 370 value 50.390622
## iter 380 value 50.202609
## iter 390 value 49.915583
## iter 400 value 49.675294
## iter 410 value 49.500596
## iter 420 value 49.385656
## iter 430 value 49.276835
## iter 440 value 49.181953
## iter 450 value 49.072194
## iter 460 value 48.923516
## iter 470 value 48.704723
## iter 480 value 48.485576
## iter 490 value 48.352389
## iter 500 value 48.312069
## final value 48.312069
## stopped after 500 iterations
## # weights: 25
## initial value 1384018.650104
## iter 10 value 15568.108680
## iter 20 value 7078.471518
## iter 30 value 5823.550517
## iter 40 value 4681.910341
## iter 50 value 3892.420056
## iter 60 value 2611.064130
## iter 70 value 2192.772551
## iter 80 value 2005.636016
## iter 90 value 1807.236078
## iter 100 value 1636.352081
## iter 110 value 1526.444834
## iter 120 value 1483.669235
## iter 130 value 1476.459207
## iter 140 value 1448.987229
## iter 150 value 1442.620774
## final value 1442.569494
## converged
## # weights: 61
## initial value 1394098.585171
## iter 10 value 26536.454537
## iter 20 value 11933.656186
## iter 30 value 8409.704475
## iter 40 value 7714.766464
## iter 50 value 6574.341375
## iter 60 value 4426.666175
## iter 70 value 3181.854615
## iter 80 value 2165.503641
## iter 90 value 1589.793031
## iter 100 value 1395.847733
## iter 110 value 1303.531922
## iter 120 value 1202.196454
## iter 130 value 1137.112382
## iter 140 value 1094.154891
## iter 150 value 1051.603814
## iter 160 value 1040.897230
## iter 170 value 1024.371791
## iter 180 value 998.908996
## iter 190 value 980.361705
## iter 200 value 968.920478
## iter 210 value 960.269924
## iter 220 value 958.949560
## iter 230 value 955.625569
## iter 240 value 954.622306
## iter 250 value 954.575117
## iter 260 value 954.545205
## final value 954.529372
## converged
## # weights: 121
## initial value 1387428.049031
## iter 10 value 1450.061966
## iter 20 value 965.028030
## iter 30 value 832.187617
## iter 40 value 742.640384
## iter 50 value 706.951827
## iter 60 value 671.165099
## iter 70 value 655.198531
## iter 80 value 640.955382
## iter 90 value 631.411994
## iter 100 value 623.066768
## iter 110 value 616.700789
## iter 120 value 608.599648
## iter 130 value 600.341217
## iter 140 value 592.731460
## iter 150 value 588.543437
## iter 160 value 584.942937
## iter 170 value 579.711382
## iter 180 value 574.896506
## iter 190 value 570.791378
## iter 200 value 565.916376
## iter 210 value 561.811951
## iter 220 value 558.347273
## iter 230 value 555.115952
## iter 240 value 552.601403
## iter 250 value 551.234121
## iter 260 value 549.812405
## iter 270 value 545.883743
## iter 280 value 543.309314
## iter 290 value 540.965899
## iter 300 value 539.338412
## iter 310 value 537.312268
## iter 320 value 534.414925
## iter 330 value 532.313464
## iter 340 value 530.388935
## iter 350 value 528.555728
## iter 360 value 527.856224
## iter 370 value 527.200364
## iter 380 value 526.704580
## iter 390 value 526.585428
## iter 400 value 526.560876
## iter 410 value 526.557217
## iter 420 value 526.556435
## iter 430 value 526.556285
## final value 526.556224
## converged
## # weights: 181
## initial value 1353091.485763
## iter 10 value 1287.823903
## iter 20 value 938.702167
## iter 30 value 748.096569
## iter 40 value 639.041680
## iter 50 value 556.282172
## iter 60 value 513.004807
## iter 70 value 483.894119
## iter 80 value 465.535296
## iter 90 value 449.084974
## iter 100 value 433.926647
## iter 110 value 425.401047
## iter 120 value 418.452940
## iter 130 value 414.162494
## iter 140 value 408.433218
## iter 150 value 404.732510
## iter 160 value 400.845451
## iter 170 value 397.982738
## iter 180 value 396.561282
## iter 190 value 395.014302
## iter 200 value 393.463659
## iter 210 value 391.791284
## iter 220 value 388.330655
## iter 230 value 386.385810
## iter 240 value 385.284808
## iter 250 value 383.783272
## iter 260 value 382.369491
## iter 270 value 381.739305
## iter 280 value 381.211069
## iter 290 value 379.185604
## iter 300 value 376.938740
## iter 310 value 376.298644
## iter 320 value 375.985631
## iter 330 value 375.767452
## iter 340 value 375.640929
## iter 350 value 375.399286
## iter 360 value 374.985965
## iter 370 value 374.872178
## iter 380 value 374.823068
## iter 390 value 374.719717
## iter 400 value 374.630241
## iter 410 value 374.580365
## iter 420 value 374.540869
## iter 430 value 374.492645
## iter 440 value 374.432551
## iter 450 value 374.330813
## iter 460 value 374.220902
## iter 470 value 374.157806
## iter 480 value 374.022897
## iter 490 value 373.172452
## iter 500 value 372.719725
## final value 372.719725
## stopped after 500 iterations
## # weights: 241
## initial value 1379427.806099
## iter 10 value 1318.482661
## iter 20 value 948.464684
## iter 30 value 781.314962
## iter 40 value 694.439588
## iter 50 value 613.809462
## iter 60 value 546.586849
## iter 70 value 499.266011
## iter 80 value 473.301240
## iter 90 value 453.074641
## iter 100 value 434.018863
## iter 110 value 422.579540
## iter 120 value 415.664194
## iter 130 value 409.582708
## iter 140 value 404.703083
## iter 150 value 400.088954
## iter 160 value 396.840796
## iter 170 value 393.550983
## iter 180 value 389.392143
## iter 190 value 384.520420
## iter 200 value 380.059544
## iter 210 value 376.737395
## iter 220 value 373.795749
## iter 230 value 371.647825
## iter 240 value 369.437417
## iter 250 value 367.198938
## iter 260 value 365.469468
## iter 270 value 363.797594
## iter 280 value 361.902571
## iter 290 value 360.302742
## iter 300 value 358.834282
## iter 310 value 357.653361
## iter 320 value 356.220173
## iter 330 value 354.742659
## iter 340 value 353.187152
## iter 350 value 352.317298
## iter 360 value 351.597133
## iter 370 value 350.748670
## iter 380 value 350.035629
## iter 390 value 349.251348
## iter 400 value 348.627508
## iter 410 value 348.035506
## iter 420 value 347.554107
## iter 430 value 347.055057
## iter 440 value 346.506821
## iter 450 value 346.028052
## iter 460 value 345.701868
## iter 470 value 345.448568
## iter 480 value 345.273019
## iter 490 value 345.171795
## iter 500 value 345.004260
## final value 345.004260
## stopped after 500 iterations
## # weights: 25
## initial value 1423891.582406
## iter 10 value 12537.489344
## iter 20 value 8269.674014
## iter 30 value 5421.520107
## iter 40 value 1535.106046
## iter 50 value 1444.459711
## iter 60 value 1406.797260
## iter 70 value 1230.493759
## iter 80 value 1084.242081
## iter 90 value 960.172332
## iter 100 value 943.333703
## iter 110 value 942.305098
## iter 120 value 930.335846
## iter 130 value 929.577196
## iter 140 value 928.927542
## iter 150 value 928.888316
## iter 160 value 928.401816
## iter 170 value 928.384803
## iter 180 value 926.787741
## iter 190 value 925.593135
## iter 200 value 925.551762
## iter 210 value 925.462572
## iter 220 value 924.972394
## iter 230 value 924.793673
## iter 240 value 921.986206
## iter 250 value 921.375885
## iter 260 value 921.237857
## iter 270 value 921.223130
## iter 280 value 921.220621
## iter 290 value 921.154089
## iter 300 value 921.110078
## iter 310 value 921.097205
## final value 921.097031
## converged
## # weights: 61
## initial value 1412917.868612
## iter 10 value 47303.215826
## iter 20 value 11772.187478
## iter 30 value 9642.666220
## iter 40 value 7039.730221
## iter 50 value 3438.511113
## iter 60 value 2153.777110
## iter 70 value 1457.745486
## iter 80 value 1089.836911
## iter 90 value 980.416563
## iter 100 value 933.168147
## iter 110 value 889.400530
## iter 120 value 870.160480
## iter 130 value 864.401688
## iter 140 value 857.626325
## iter 150 value 850.453945
## iter 160 value 841.635431
## iter 170 value 821.291746
## iter 180 value 805.288555
## iter 190 value 800.439409
## iter 200 value 793.712157
## iter 210 value 782.194383
## iter 220 value 776.109391
## iter 230 value 765.630829
## iter 240 value 753.827264
## iter 250 value 750.095525
## iter 260 value 747.055166
## iter 270 value 744.176972
## iter 280 value 738.447826
## iter 290 value 730.003868
## iter 300 value 716.537518
## iter 310 value 715.365196
## iter 320 value 713.454410
## iter 330 value 711.280430
## iter 340 value 709.210457
## iter 350 value 707.570229
## iter 360 value 706.449745
## iter 370 value 705.894708
## iter 380 value 705.207222
## iter 390 value 705.024693
## iter 400 value 704.997233
## iter 410 value 704.995416
## iter 420 value 704.994876
## iter 420 value 704.994873
## iter 420 value 704.994873
## final value 704.994873
## converged
## # weights: 121
## initial value 1433584.753726
## iter 10 value 2356.920606
## iter 20 value 1146.044953
## iter 30 value 894.843159
## iter 40 value 748.909966
## iter 50 value 659.434098
## iter 60 value 583.222552
## iter 70 value 521.519816
## iter 80 value 490.932249
## iter 90 value 466.770159
## iter 100 value 449.518369
## iter 110 value 421.297782
## iter 120 value 396.615299
## iter 130 value 383.763143
## iter 140 value 376.869871
## iter 150 value 365.351570
## iter 160 value 356.752188
## iter 170 value 347.514557
## iter 180 value 339.875712
## iter 190 value 334.620596
## iter 200 value 327.643579
## iter 210 value 324.005633
## iter 220 value 321.439653
## iter 230 value 318.718763
## iter 240 value 316.720846
## iter 250 value 316.349474
## iter 260 value 316.258847
## iter 270 value 316.004443
## iter 280 value 315.429764
## iter 290 value 314.608690
## iter 300 value 314.017114
## iter 310 value 313.057631
## iter 320 value 311.650614
## iter 330 value 303.149669
## iter 340 value 292.307685
## iter 350 value 283.880966
## iter 360 value 278.707246
## iter 370 value 275.872878
## iter 380 value 274.003154
## iter 390 value 272.594295
## iter 400 value 271.087957
## iter 410 value 269.693184
## iter 420 value 268.877864
## iter 430 value 267.877948
## iter 440 value 267.495669
## iter 450 value 267.155985
## iter 460 value 266.930277
## iter 470 value 266.848237
## iter 480 value 266.813716
## iter 490 value 266.803428
## iter 500 value 266.802076
## final value 266.802076
## stopped after 500 iterations
## # weights: 181
## initial value 1394930.794764
## iter 10 value 1279.269553
## iter 20 value 868.323818
## iter 30 value 719.556231
## iter 40 value 561.315354
## iter 50 value 464.560336
## iter 60 value 412.004354
## iter 70 value 373.089421
## iter 80 value 327.225673
## iter 90 value 296.791422
## iter 100 value 275.365839
## iter 110 value 258.632874
## iter 120 value 247.519543
## iter 130 value 238.913534
## iter 140 value 224.320611
## iter 150 value 211.431281
## iter 160 value 195.649973
## iter 170 value 184.992278
## iter 180 value 179.439211
## iter 190 value 174.741821
## iter 200 value 171.307787
## iter 210 value 167.550830
## iter 220 value 164.440970
## iter 230 value 162.559312
## iter 240 value 159.905158
## iter 250 value 158.674720
## iter 260 value 157.792217
## iter 270 value 156.438216
## iter 280 value 154.814089
## iter 290 value 153.285061
## iter 300 value 152.266027
## iter 310 value 151.416318
## iter 320 value 150.592960
## iter 330 value 149.935923
## iter 340 value 149.200792
## iter 350 value 148.616745
## iter 360 value 148.272016
## iter 370 value 148.148050
## iter 380 value 148.073069
## iter 390 value 147.871002
## iter 400 value 147.648666
## iter 410 value 147.288195
## iter 420 value 146.711104
## iter 430 value 145.937980
## iter 440 value 145.451685
## iter 450 value 144.939674
## iter 460 value 144.271039
## iter 470 value 143.147613
## iter 480 value 141.523488
## iter 490 value 139.291811
## iter 500 value 136.917199
## final value 136.917199
## stopped after 500 iterations
## # weights: 241
## initial value 1415447.001248
## iter 10 value 1560.997067
## iter 20 value 900.702298
## iter 30 value 647.249066
## iter 40 value 492.717841
## iter 50 value 405.391326
## iter 60 value 345.438590
## iter 70 value 306.750507
## iter 80 value 267.845476
## iter 90 value 235.799128
## iter 100 value 203.677835
## iter 110 value 183.091395
## iter 120 value 166.683630
## iter 130 value 150.230650
## iter 140 value 138.325315
## iter 150 value 126.601246
## iter 160 value 117.089372
## iter 170 value 109.712789
## iter 180 value 104.980911
## iter 190 value 100.729998
## iter 200 value 97.313505
## iter 210 value 94.693012
## iter 220 value 91.855427
## iter 230 value 88.698395
## iter 240 value 86.415252
## iter 250 value 84.194762
## iter 260 value 81.902251
## iter 270 value 79.161843
## iter 280 value 76.602614
## iter 290 value 74.852013
## iter 300 value 72.364602
## iter 310 value 70.525242
## iter 320 value 69.077758
## iter 330 value 67.575295
## iter 340 value 66.004828
## iter 350 value 64.995929
## iter 360 value 64.086367
## iter 370 value 63.280264
## iter 380 value 62.223884
## iter 390 value 61.269493
## iter 400 value 60.774563
## iter 410 value 59.812007
## iter 420 value 58.774771
## iter 430 value 58.390361
## iter 440 value 58.186240
## iter 450 value 58.035690
## iter 460 value 57.846932
## iter 470 value 57.675788
## iter 480 value 57.473200
## iter 490 value 57.338265
## iter 500 value 57.308874
## final value 57.308874
## stopped after 500 iterations
## # weights: 25
## initial value 1386870.681414
## iter 10 value 16073.496682
## iter 20 value 15376.477898
## iter 30 value 11345.105417
## iter 40 value 10198.597925
## iter 50 value 6428.790073
## iter 60 value 5644.346354
## iter 70 value 5497.965330
## iter 80 value 5421.132636
## iter 90 value 5179.625122
## iter 100 value 4884.074564
## iter 110 value 4234.055230
## iter 120 value 2904.598017
## iter 130 value 1556.481872
## iter 140 value 1388.490514
## iter 150 value 1364.959883
## iter 160 value 1354.867166
## iter 170 value 1328.410308
## iter 180 value 1319.516961
## iter 190 value 1314.239222
## iter 200 value 1311.288587
## iter 210 value 1309.937188
## iter 220 value 1309.893532
## iter 230 value 1307.847786
## iter 240 value 1294.078490
## iter 250 value 1283.816175
## iter 260 value 1278.919102
## iter 270 value 1270.853058
## iter 280 value 1258.307996
## iter 290 value 1252.139480
## iter 300 value 1232.788080
## iter 310 value 1199.651938
## iter 320 value 1172.256003
## iter 330 value 1166.258096
## iter 340 value 1163.088009
## iter 350 value 1162.089573
## iter 360 value 1160.698645
## iter 370 value 1133.349758
## iter 380 value 1079.030491
## iter 390 value 1074.131190
## iter 400 value 1073.353709
## iter 410 value 1072.970806
## iter 420 value 1070.957063
## iter 430 value 1068.827441
## iter 440 value 1068.618943
## final value 1068.618146
## converged
## # weights: 61
## initial value 1369944.309207
## iter 10 value 4813.258129
## iter 20 value 2647.028707
## iter 30 value 2018.882759
## iter 40 value 1448.844785
## iter 50 value 1310.679541
## iter 60 value 1237.240533
## iter 70 value 1144.330366
## iter 80 value 930.396868
## iter 90 value 844.047470
## iter 100 value 813.793452
## iter 110 value 799.122112
## iter 120 value 791.694476
## iter 130 value 785.215152
## iter 140 value 780.425639
## iter 150 value 773.020497
## iter 160 value 768.206613
## iter 170 value 755.018782
## iter 180 value 753.053456
## iter 190 value 749.706268
## iter 200 value 748.552178
## iter 210 value 741.293601
## iter 220 value 722.928566
## iter 230 value 718.856657
## iter 240 value 711.482516
## iter 250 value 703.614285
## iter 260 value 688.171332
## iter 270 value 686.786797
## iter 280 value 686.583852
## iter 290 value 686.442883
## iter 300 value 686.361656
## iter 310 value 684.560410
## iter 320 value 676.478486
## iter 330 value 675.804383
## iter 340 value 675.380648
## iter 350 value 674.316048
## iter 360 value 673.271927
## iter 370 value 672.798969
## iter 380 value 672.329418
## iter 390 value 672.184275
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## iter 490 value 671.482352
## iter 500 value 671.480801
## final value 671.480801
## stopped after 500 iterations
## # weights: 121
## initial value 1448634.114928
## iter 10 value 13322.704711
## iter 20 value 4075.345309
## iter 30 value 3080.568831
## iter 40 value 2774.136853
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## iter 500 value 585.092013
## final value 585.092013
## stopped after 500 iterations
## # weights: 181
## initial value 1413917.762241
## iter 10 value 1208.414670
## iter 20 value 780.378653
## iter 30 value 659.898262
## iter 40 value 534.589345
## iter 50 value 438.795352
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## iter 500 value 142.279220
## final value 142.279220
## stopped after 500 iterations
## # weights: 241
## initial value 1408462.442303
## iter 10 value 1696.574721
## iter 20 value 834.666584
## iter 30 value 643.461071
## iter 40 value 499.893272
## iter 50 value 396.200813
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## iter 330 value 61.827949
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## iter 380 value 53.266239
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## iter 400 value 51.581962
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## iter 460 value 46.564369
## iter 470 value 46.010227
## iter 480 value 45.543231
## iter 490 value 45.250383
## iter 500 value 45.190447
## final value 45.190447
## stopped after 500 iterations
## # weights: 25
## initial value 1372476.830600
## iter 10 value 66071.850729
## iter 20 value 16301.884162
## iter 30 value 15924.138647
## iter 40 value 15669.361705
## iter 50 value 15504.252382
## iter 60 value 14128.677558
## iter 70 value 12415.066884
## iter 80 value 11731.682723
## iter 90 value 11597.027862
## iter 100 value 11542.573502
## iter 110 value 11516.507493
## final value 11516.361465
## converged
## # weights: 61
## initial value 1407938.264648
## iter 10 value 2843.758487
## iter 20 value 1843.940204
## iter 30 value 1205.152712
## iter 40 value 960.451207
## iter 50 value 866.733606
## iter 60 value 752.674082
## iter 70 value 726.720860
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## iter 90 value 717.945696
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## iter 110 value 691.123577
## iter 120 value 668.661251
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## iter 190 value 620.068787
## iter 200 value 609.319300
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## iter 220 value 599.195914
## iter 230 value 598.074594
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## iter 250 value 595.768566
## iter 260 value 595.725856
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## iter 290 value 595.038863
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## iter 310 value 592.675694
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## iter 330 value 591.226827
## iter 340 value 590.903815
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## iter 480 value 584.398097
## iter 490 value 584.299566
## iter 500 value 583.819417
## final value 583.819417
## stopped after 500 iterations
## # weights: 121
## initial value 1400959.315384
## iter 10 value 3680.038454
## iter 20 value 1607.147930
## iter 30 value 1149.990065
## iter 40 value 880.060931
## iter 50 value 765.302278
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## iter 90 value 572.317587
## iter 100 value 541.210496
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## iter 220 value 453.628710
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## iter 470 value 433.595156
## iter 480 value 433.579809
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## iter 500 value 433.568055
## final value 433.568055
## stopped after 500 iterations
## # weights: 181
## initial value 1436380.947420
## iter 10 value 1396.649747
## iter 20 value 780.122467
## iter 30 value 624.376353
## iter 40 value 498.828115
## iter 50 value 436.726747
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## iter 80 value 316.159909
## iter 90 value 293.793647
## iter 100 value 279.920857
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## iter 480 value 189.502272
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## iter 500 value 188.874484
## final value 188.874484
## stopped after 500 iterations
## # weights: 241
## initial value 1361337.236566
## iter 10 value 1219.972364
## iter 20 value 853.936547
## iter 30 value 638.681809
## iter 40 value 510.339447
## iter 50 value 429.476184
## iter 60 value 354.555508
## iter 70 value 294.870702
## iter 80 value 252.298186
## iter 90 value 217.298982
## iter 100 value 185.503750
## iter 110 value 170.873262
## iter 120 value 157.969799
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## iter 140 value 131.943708
## iter 150 value 117.770202
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## iter 170 value 85.934729
## iter 180 value 74.247126
## iter 190 value 68.101719
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## iter 250 value 51.804822
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## iter 320 value 42.143912
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## iter 340 value 40.824645
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## iter 400 value 39.451142
## iter 410 value 39.303839
## iter 420 value 39.101274
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## iter 440 value 38.328375
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## iter 460 value 37.704041
## iter 470 value 37.506902
## iter 480 value 37.394181
## iter 490 value 37.358919
## iter 500 value 37.349176
## final value 37.349176
## stopped after 500 iterations
## # weights: 25
## initial value 1387099.627384
## iter 10 value 6884.863381
## iter 20 value 5742.708364
## iter 30 value 5648.924554
## iter 40 value 5489.348356
## iter 50 value 5294.227643
## iter 60 value 4191.619062
## iter 70 value 3439.820488
## iter 80 value 1865.159081
## iter 90 value 1354.174023
## iter 100 value 1230.275976
## iter 110 value 1213.606377
## iter 120 value 1203.457053
## iter 130 value 1172.423722
## iter 140 value 1157.156416
## iter 150 value 1149.554267
## iter 160 value 1148.385147
## iter 170 value 1145.328761
## iter 180 value 1133.782683
## iter 190 value 1115.050236
## iter 200 value 1098.741020
## iter 210 value 1071.941371
## iter 220 value 1069.565696
## iter 230 value 1068.972566
## iter 240 value 1065.824168
## iter 250 value 1063.384913
## iter 260 value 1061.492019
## iter 270 value 1061.043238
## iter 280 value 1061.026919
## iter 290 value 1060.383288
## iter 300 value 1059.598781
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## iter 320 value 1058.548991
## iter 330 value 1058.546728
## iter 340 value 1058.471061
## iter 350 value 1057.953294
## iter 360 value 1057.525014
## iter 370 value 1057.265463
## iter 380 value 1057.261163
## iter 380 value 1057.261161
## iter 380 value 1057.261157
## final value 1057.261157
## converged
## # weights: 61
## initial value 1409061.029459
## iter 10 value 1454.501398
## iter 20 value 1173.662421
## iter 30 value 948.423994
## iter 40 value 818.321733
## iter 50 value 723.675688
## iter 60 value 669.952133
## iter 70 value 632.214352
## iter 80 value 621.930101
## iter 90 value 612.884284
## iter 100 value 610.410488
## iter 110 value 608.256095
## iter 120 value 607.334157
## iter 130 value 606.911674
## iter 140 value 606.777125
## iter 150 value 606.119864
## iter 160 value 604.317289
## iter 170 value 600.597374
## iter 180 value 593.442563
## iter 190 value 589.851119
## iter 200 value 587.664736
## iter 210 value 585.790198
## iter 220 value 584.125603
## iter 230 value 582.950025
## iter 240 value 582.386864
## iter 250 value 582.116854
## iter 260 value 582.086040
## iter 270 value 581.971393
## iter 280 value 581.690192
## iter 290 value 581.460932
## iter 300 value 581.353291
## iter 310 value 581.149949
## iter 320 value 580.848503
## iter 330 value 580.611379
## iter 340 value 580.393534
## iter 350 value 580.266394
## iter 360 value 580.141481
## iter 370 value 579.957066
## iter 380 value 579.953258
## iter 390 value 579.941049
## iter 400 value 579.880828
## iter 410 value 579.788238
## iter 420 value 579.685639
## iter 430 value 579.303637
## iter 440 value 579.152107
## iter 450 value 578.975600
## iter 460 value 578.868404
## iter 470 value 578.775307
## iter 480 value 578.687300
## iter 490 value 578.665848
## final value 578.663563
## converged
## # weights: 121
## initial value 1416636.374166
## iter 10 value 1671.478236
## iter 20 value 1000.629062
## iter 30 value 821.121997
## iter 40 value 713.814097
## iter 50 value 641.158097
## iter 60 value 608.680645
## iter 70 value 586.740869
## iter 80 value 567.069916
## iter 90 value 553.661699
## iter 100 value 545.983177
## iter 110 value 542.387548
## iter 120 value 538.795445
## iter 130 value 532.736078
## iter 140 value 527.976666
## iter 150 value 518.717146
## iter 160 value 509.550051
## iter 170 value 494.733760
## iter 180 value 487.147887
## iter 190 value 468.792136
## iter 200 value 454.176554
## iter 210 value 446.169427
## iter 220 value 438.455061
## iter 230 value 430.930929
## iter 240 value 415.480240
## iter 250 value 394.001123
## iter 260 value 387.039195
## iter 270 value 384.757568
## iter 280 value 382.687809
## iter 290 value 379.690969
## iter 300 value 378.106648
## iter 310 value 377.187713
## iter 320 value 375.975369
## iter 330 value 374.850924
## iter 340 value 374.210535
## iter 350 value 373.882347
## iter 360 value 373.618886
## iter 370 value 373.397226
## iter 380 value 373.393873
## iter 390 value 373.385757
## iter 400 value 373.367752
## iter 410 value 373.349413
## iter 420 value 373.338195
## iter 430 value 373.331903
## iter 440 value 373.321640
## iter 450 value 373.295236
## iter 460 value 373.258294
## iter 470 value 372.411457
## iter 480 value 371.348802
## iter 490 value 370.902117
## iter 500 value 370.609300
## final value 370.609300
## stopped after 500 iterations
## # weights: 181
## initial value 1381719.005892
## iter 10 value 1467.172498
## iter 20 value 786.635499
## iter 30 value 620.697677
## iter 40 value 515.593629
## iter 50 value 431.749592
## iter 60 value 362.889480
## iter 70 value 319.911837
## iter 80 value 275.422756
## iter 90 value 248.714420
## iter 100 value 230.661755
## iter 110 value 212.389696
## iter 120 value 200.509538
## iter 130 value 188.212657
## iter 140 value 178.478457
## iter 150 value 166.196181
## iter 160 value 155.862943
## iter 170 value 144.901767
## iter 180 value 134.984710
## iter 190 value 128.138283
## iter 200 value 123.781473
## iter 210 value 120.680937
## iter 220 value 116.928077
## iter 230 value 114.145371
## iter 240 value 112.195092
## iter 250 value 110.962329
## iter 260 value 108.867038
## iter 270 value 107.311021
## iter 280 value 106.083067
## iter 290 value 105.115520
## iter 300 value 104.299323
## iter 310 value 103.735284
## iter 320 value 103.177508
## iter 330 value 102.788422
## iter 340 value 102.494317
## iter 350 value 102.231510
## iter 360 value 102.058439
## iter 370 value 101.984354
## iter 380 value 101.943327
## iter 390 value 101.862886
## iter 400 value 101.732656
## iter 410 value 101.610271
## iter 420 value 101.527765
## iter 430 value 101.409239
## iter 440 value 101.299359
## iter 450 value 101.010618
## iter 460 value 100.526020
## iter 470 value 100.185451
## iter 480 value 99.605011
## iter 490 value 99.192383
## iter 500 value 98.901575
## final value 98.901575
## stopped after 500 iterations
## # weights: 241
## initial value 1406575.679480
## iter 10 value 1475.842726
## iter 20 value 810.414268
## iter 30 value 656.808281
## iter 40 value 565.655535
## iter 50 value 446.121098
## iter 60 value 353.930487
## iter 70 value 297.073127
## iter 80 value 264.388584
## iter 90 value 236.573331
## iter 100 value 202.989764
## iter 110 value 183.267716
## iter 120 value 169.714214
## iter 130 value 160.201311
## iter 140 value 151.958332
## iter 150 value 144.941023
## iter 160 value 139.131706
## iter 170 value 133.951261
## iter 180 value 129.112631
## iter 190 value 121.705868
## iter 200 value 114.814578
## iter 210 value 109.074391
## iter 220 value 104.472022
## iter 230 value 101.252522
## iter 240 value 97.580645
## iter 250 value 94.504699
## iter 260 value 92.713906
## iter 270 value 91.167732
## iter 280 value 88.978635
## iter 290 value 87.362266
## iter 300 value 86.082114
## iter 310 value 84.627525
## iter 320 value 83.374603
## iter 330 value 81.261878
## iter 340 value 79.186915
## iter 350 value 78.093975
## iter 360 value 76.713596
## iter 370 value 74.926649
## iter 380 value 72.850564
## iter 390 value 71.190179
## iter 400 value 69.651247
## iter 410 value 68.466478
## iter 420 value 67.145333
## iter 430 value 65.799797
## iter 440 value 64.284767
## iter 450 value 63.308524
## iter 460 value 62.314202
## iter 470 value 61.325149
## iter 480 value 60.164964
## iter 490 value 59.507512
## iter 500 value 59.326190
## final value 59.326190
## stopped after 500 iterations
## # weights: 25
## initial value 1383669.468917
## iter 10 value 19610.500507
## iter 20 value 14471.155774
## iter 30 value 10709.210303
## iter 40 value 10191.043565
## iter 50 value 6046.932930
## iter 60 value 4952.313284
## iter 70 value 3729.353329
## iter 80 value 2648.268330
## iter 90 value 2088.731714
## iter 100 value 1884.026272
## iter 110 value 1676.235137
## iter 120 value 1401.763556
## iter 130 value 1364.790876
## iter 140 value 1355.966270
## iter 150 value 1352.899702
## iter 160 value 1334.776159
## iter 170 value 1315.688009
## iter 180 value 1311.984388
## iter 190 value 1311.759084
## iter 200 value 1311.742942
## iter 210 value 1311.607034
## final value 1311.605220
## converged
## # weights: 61
## initial value 1386502.060133
## iter 10 value 9740.599426
## iter 20 value 5854.369773
## iter 30 value 4467.394927
## iter 40 value 3972.388546
## iter 50 value 2908.281894
## iter 60 value 1997.864900
## iter 70 value 1432.078601
## iter 80 value 1093.070390
## iter 90 value 925.424098
## iter 100 value 848.214016
## iter 110 value 825.406624
## iter 120 value 813.197770
## iter 130 value 803.037929
## iter 140 value 797.717340
## iter 150 value 796.269914
## iter 160 value 795.568900
## iter 170 value 795.506408
## iter 180 value 795.359322
## iter 190 value 795.229246
## iter 200 value 795.167448
## final value 795.163462
## converged
## # weights: 121
## initial value 1413503.513575
## iter 10 value 3403.502379
## iter 20 value 1782.465199
## iter 30 value 1394.351447
## iter 40 value 1237.435546
## iter 50 value 1143.684364
## iter 60 value 1095.687901
## iter 70 value 980.102309
## iter 80 value 804.775659
## iter 90 value 727.451692
## iter 100 value 649.523058
## iter 110 value 605.151559
## iter 120 value 581.368095
## iter 130 value 560.948501
## iter 140 value 538.796003
## iter 150 value 525.258830
## iter 160 value 518.871315
## iter 170 value 512.737274
## iter 180 value 510.199211
## iter 190 value 507.535789
## iter 200 value 498.924478
## iter 210 value 492.578750
## iter 220 value 489.662353
## iter 230 value 484.872188
## iter 240 value 477.891235
## iter 250 value 473.021551
## iter 260 value 470.758371
## iter 270 value 468.759570
## iter 280 value 462.677149
## iter 290 value 458.149118
## iter 300 value 455.809965
## iter 310 value 455.062626
## iter 320 value 453.919139
## iter 330 value 452.168341
## iter 340 value 450.792724
## iter 350 value 448.140931
## iter 360 value 445.935086
## iter 370 value 444.882889
## iter 380 value 442.779359
## iter 390 value 441.208245
## iter 400 value 439.674456
## iter 410 value 437.653320
## iter 420 value 436.556541
## iter 430 value 436.216592
## iter 440 value 436.143452
## iter 450 value 436.129581
## iter 460 value 436.129288
## final value 436.129259
## converged
## # weights: 181
## initial value 1341344.982892
## iter 10 value 1178.530156
## iter 20 value 857.914268
## iter 30 value 716.172612
## iter 40 value 585.632401
## iter 50 value 517.967504
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## iter 100 value 402.736674
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## iter 470 value 327.861521
## iter 480 value 327.777054
## iter 490 value 327.729097
## iter 500 value 327.714027
## final value 327.714027
## stopped after 500 iterations
## # weights: 241
## initial value 1423336.286128
## iter 10 value 1851.926684
## iter 20 value 1016.069858
## iter 30 value 876.867018
## iter 40 value 713.671169
## iter 50 value 644.062703
## iter 60 value 605.523475
## iter 70 value 572.504108
## iter 80 value 546.979620
## iter 90 value 512.908416
## iter 100 value 490.775363
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## iter 220 value 431.906909
## iter 230 value 429.154806
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## iter 250 value 416.313930
## iter 260 value 408.862144
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## iter 300 value 380.353348
## iter 310 value 376.616421
## iter 320 value 372.914839
## iter 330 value 369.645424
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## iter 400 value 352.029597
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## iter 470 value 335.804255
## iter 480 value 334.177821
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## iter 500 value 332.497097
## final value 332.497097
## stopped after 500 iterations
## # weights: 25
## initial value 1375188.420772
## iter 10 value 16119.511031
## iter 20 value 15880.421201
## iter 30 value 15062.229959
## iter 40 value 13267.068718
## iter 50 value 11659.520583
## iter 60 value 11489.160385
## iter 70 value 11184.906299
## iter 80 value 11021.728167
## iter 90 value 9136.261094
## iter 100 value 7171.549235
## iter 110 value 3412.599879
## iter 120 value 3087.397655
## iter 130 value 2761.127256
## iter 140 value 1604.437857
## iter 150 value 1118.981524
## iter 160 value 1049.019514
## iter 170 value 1029.997241
## iter 180 value 1028.590950
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## iter 200 value 1012.277231
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## iter 220 value 997.385867
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## iter 470 value 824.266305
## iter 480 value 819.949615
## iter 490 value 818.219794
## iter 500 value 817.865796
## final value 817.865796
## stopped after 500 iterations
## # weights: 61
## initial value 1399511.976977
## iter 10 value 3294.217753
## iter 20 value 1040.022472
## iter 30 value 864.268705
## iter 40 value 792.876915
## iter 50 value 732.249784
## iter 60 value 696.854187
## iter 70 value 657.797294
## iter 80 value 633.222310
## iter 90 value 622.959040
## iter 100 value 606.550763
## iter 110 value 595.308315
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## iter 130 value 582.658232
## iter 140 value 580.469537
## iter 150 value 577.411224
## iter 160 value 573.015636
## iter 170 value 568.183093
## iter 180 value 563.184305
## iter 190 value 557.760849
## iter 200 value 556.321240
## iter 210 value 553.737665
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## iter 230 value 550.088426
## iter 240 value 549.774181
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## iter 290 value 548.268939
## iter 300 value 547.965162
## iter 310 value 547.647210
## iter 320 value 547.448586
## iter 330 value 547.394297
## iter 340 value 547.375884
## iter 350 value 547.372380
## iter 360 value 547.370778
## final value 547.370379
## converged
## # weights: 121
## initial value 1318817.049339
## iter 10 value 1453.391468
## iter 20 value 921.729845
## iter 30 value 703.977163
## iter 40 value 593.417066
## iter 50 value 550.198590
## iter 60 value 505.727508
## iter 70 value 468.703416
## iter 80 value 443.217625
## iter 90 value 424.657523
## iter 100 value 403.152947
## iter 110 value 383.859944
## iter 120 value 372.970880
## iter 130 value 364.821421
## iter 140 value 354.805915
## iter 150 value 349.642399
## iter 160 value 344.135042
## iter 170 value 340.856247
## iter 180 value 339.335419
## iter 190 value 337.513490
## iter 200 value 335.723795
## iter 210 value 334.423805
## iter 220 value 333.553679
## iter 230 value 331.104395
## iter 240 value 328.718307
## iter 250 value 328.180533
## iter 260 value 327.685124
## iter 270 value 326.494534
## iter 280 value 325.364988
## iter 290 value 324.406136
## iter 300 value 322.301884
## iter 310 value 320.781151
## iter 320 value 318.420135
## iter 330 value 316.643753
## iter 340 value 314.956362
## iter 350 value 313.183301
## iter 360 value 311.139530
## iter 370 value 310.386860
## iter 380 value 309.764422
## iter 390 value 308.243278
## iter 400 value 308.018837
## iter 410 value 307.967262
## iter 420 value 307.952379
## iter 430 value 307.909690
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## iter 450 value 307.884472
## iter 460 value 307.883309
## iter 470 value 307.883103
## iter 480 value 307.882997
## iter 480 value 307.882995
## iter 480 value 307.882995
## final value 307.882995
## converged
## # weights: 181
## initial value 1419080.413303
## iter 10 value 1100.036839
## iter 20 value 764.218444
## iter 30 value 628.233927
## iter 40 value 496.085788
## iter 50 value 417.574751
## iter 60 value 378.683439
## iter 70 value 317.964373
## iter 80 value 271.928488
## iter 90 value 246.929342
## iter 100 value 231.459760
## iter 110 value 219.348598
## iter 120 value 210.202946
## iter 130 value 202.782278
## iter 140 value 193.502618
## iter 150 value 186.078258
## iter 160 value 180.239895
## iter 170 value 175.942782
## iter 180 value 171.271571
## iter 190 value 168.306903
## iter 200 value 164.965320
## iter 210 value 162.798508
## iter 220 value 160.623448
## iter 230 value 159.490941
## iter 240 value 158.087511
## iter 250 value 155.869789
## iter 260 value 154.031411
## iter 270 value 152.792370
## iter 280 value 151.734751
## iter 290 value 149.337524
## iter 300 value 146.053316
## iter 310 value 140.735076
## iter 320 value 137.470049
## iter 330 value 135.058348
## iter 340 value 133.284646
## iter 350 value 131.965696
## iter 360 value 130.674931
## iter 370 value 130.267534
## iter 380 value 130.089457
## iter 390 value 129.680118
## iter 400 value 129.206069
## iter 410 value 128.657836
## iter 420 value 127.952362
## iter 430 value 127.250826
## iter 440 value 126.691712
## iter 450 value 125.611973
## iter 460 value 124.752862
## iter 470 value 123.890380
## iter 480 value 123.030975
## iter 490 value 122.161475
## iter 500 value 121.987209
## final value 121.987209
## stopped after 500 iterations
## # weights: 241
## initial value 1397038.290086
## iter 10 value 1211.378573
## iter 20 value 773.976683
## iter 30 value 590.106615
## iter 40 value 447.123158
## iter 50 value 356.949966
## iter 60 value 317.098232
## iter 70 value 282.110668
## iter 80 value 253.652867
## iter 90 value 230.618855
## iter 100 value 210.181163
## iter 110 value 190.277420
## iter 120 value 178.968309
## iter 130 value 168.359989
## iter 140 value 160.503202
## iter 150 value 154.950933
## iter 160 value 150.330774
## iter 170 value 146.556252
## iter 180 value 138.400497
## iter 190 value 130.060627
## iter 200 value 122.363227
## iter 210 value 114.359536
## iter 220 value 109.616771
## iter 230 value 105.506047
## iter 240 value 99.172951
## iter 250 value 95.629248
## iter 260 value 92.842606
## iter 270 value 89.888847
## iter 280 value 87.361913
## iter 290 value 85.397006
## iter 300 value 84.127628
## iter 310 value 82.626720
## iter 320 value 81.320245
## iter 330 value 79.712122
## iter 340 value 78.589361
## iter 350 value 77.536845
## iter 360 value 76.627702
## iter 370 value 76.035352
## iter 380 value 75.369379
## iter 390 value 74.844172
## iter 400 value 74.362881
## iter 410 value 73.927568
## iter 420 value 73.509443
## iter 430 value 73.182551
## iter 440 value 72.812802
## iter 450 value 72.452923
## iter 460 value 72.044837
## iter 470 value 71.591822
## iter 480 value 71.265800
## iter 490 value 71.136214
## iter 500 value 71.103190
## final value 71.103190
## stopped after 500 iterations
## # weights: 25
## initial value 1403525.145478
## iter 10 value 21688.413349
## iter 20 value 18074.027468
## iter 30 value 13253.667308
## iter 40 value 5355.576130
## iter 50 value 4198.805374
## iter 60 value 2884.583194
## iter 70 value 1664.628362
## iter 80 value 1285.733297
## iter 90 value 1216.410422
## iter 100 value 1197.994005
## iter 110 value 1144.330453
## iter 120 value 1108.928339
## iter 130 value 1074.456607
## iter 140 value 1055.127880
## iter 150 value 1051.987813
## iter 160 value 1004.515674
## iter 170 value 961.179760
## iter 180 value 952.693826
## iter 190 value 943.785455
## iter 200 value 943.700237
## iter 210 value 937.173331
## iter 220 value 930.391885
## iter 230 value 929.316788
## iter 240 value 928.531134
## iter 250 value 928.530112
## iter 260 value 928.524564
## final value 928.524492
## converged
## # weights: 61
## initial value 1366580.530735
## iter 10 value 4025.099451
## iter 20 value 3381.118706
## iter 30 value 3067.156812
## iter 40 value 2276.477332
## iter 50 value 1789.455966
## iter 60 value 1412.358155
## iter 70 value 1180.183655
## iter 80 value 1128.216822
## iter 90 value 1108.572847
## iter 100 value 1095.400367
## iter 110 value 1086.631911
## iter 120 value 1080.125022
## iter 130 value 1078.936110
## iter 140 value 1078.142800
## iter 150 value 1075.182631
## iter 160 value 1072.198550
## iter 170 value 1070.464567
## iter 180 value 1069.251143
## iter 190 value 1067.745711
## iter 200 value 1064.645319
## iter 210 value 1063.811087
## iter 220 value 1059.156459
## iter 230 value 958.537170
## iter 240 value 835.448039
## iter 250 value 768.284908
## iter 260 value 759.983483
## iter 270 value 757.164801
## iter 280 value 756.291263
## iter 290 value 756.122870
## iter 300 value 754.684904
## iter 310 value 753.736673
## iter 320 value 748.001538
## iter 330 value 747.739831
## iter 340 value 746.181934
## iter 350 value 742.723602
## iter 360 value 738.673894
## iter 370 value 737.319722
## iter 380 value 736.376307
## iter 390 value 734.981637
## iter 400 value 734.068286
## iter 410 value 734.037284
## iter 420 value 733.678072
## iter 430 value 731.830443
## iter 440 value 729.350139
## iter 450 value 729.025183
## iter 460 value 728.941798
## iter 470 value 728.760947
## iter 480 value 728.617584
## iter 490 value 728.443569
## iter 500 value 728.165814
## final value 728.165814
## stopped after 500 iterations
## # weights: 121
## initial value 1428452.421636
## iter 10 value 6383.787906
## iter 20 value 2051.752399
## iter 30 value 1125.636217
## iter 40 value 874.564366
## iter 50 value 691.287940
## iter 60 value 590.589031
## iter 70 value 531.690486
## iter 80 value 461.855606
## iter 90 value 441.176574
## iter 100 value 402.723929
## iter 110 value 391.707042
## iter 120 value 382.528883
## iter 130 value 373.526151
## iter 140 value 363.883340
## iter 150 value 355.555027
## iter 160 value 344.479368
## iter 170 value 341.230173
## iter 180 value 338.384354
## iter 190 value 333.268233
## iter 200 value 328.019198
## iter 210 value 322.607345
## iter 220 value 317.512169
## iter 230 value 315.307060
## iter 240 value 312.647665
## iter 250 value 311.712684
## iter 260 value 311.293612
## iter 270 value 310.587679
## iter 280 value 309.090897
## iter 290 value 304.860667
## iter 300 value 299.562820
## iter 310 value 293.597498
## iter 320 value 291.253364
## iter 330 value 290.269132
## iter 340 value 289.006626
## iter 350 value 287.202325
## iter 360 value 285.613001
## iter 370 value 284.064400
## iter 380 value 282.898085
## iter 390 value 282.443246
## iter 400 value 282.230639
## iter 410 value 282.132799
## iter 420 value 282.024003
## iter 430 value 281.762204
## iter 440 value 281.347235
## iter 450 value 281.232505
## iter 460 value 281.178753
## iter 470 value 281.117442
## iter 480 value 281.056637
## iter 490 value 281.021164
## iter 500 value 281.019280
## final value 281.019280
## stopped after 500 iterations
## # weights: 181
## initial value 1404235.414039
## iter 10 value 1033.468879
## iter 20 value 732.040510
## iter 30 value 591.746626
## iter 40 value 449.667585
## iter 50 value 378.525598
## iter 60 value 339.014724
## iter 70 value 295.532887
## iter 80 value 263.315277
## iter 90 value 243.927583
## iter 100 value 225.755741
## iter 110 value 204.605987
## iter 120 value 182.767911
## iter 130 value 171.237414
## iter 140 value 164.273453
## iter 150 value 156.734336
## iter 160 value 152.035480
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## iter 180 value 143.038288
## iter 190 value 138.596944
## iter 200 value 133.930733
## iter 210 value 131.345534
## iter 220 value 127.222502
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## iter 250 value 113.661619
## iter 260 value 111.375504
## iter 270 value 108.863356
## iter 280 value 107.291148
## iter 290 value 104.740459
## iter 300 value 102.986607
## iter 310 value 101.241333
## iter 320 value 99.959635
## iter 330 value 98.152128
## iter 340 value 97.034264
## iter 350 value 96.205096
## iter 360 value 95.476872
## iter 370 value 95.220009
## iter 380 value 95.108877
## iter 390 value 94.853611
## iter 400 value 94.569762
## iter 410 value 94.297014
## iter 420 value 93.868959
## iter 430 value 93.584574
## iter 440 value 93.292902
## iter 450 value 93.130017
## iter 460 value 92.906305
## iter 470 value 92.652221
## iter 480 value 92.393109
## iter 490 value 92.129343
## iter 500 value 91.597290
## final value 91.597290
## stopped after 500 iterations
## # weights: 241
## initial value 1297007.387561
## iter 10 value 1363.616777
## iter 20 value 845.703911
## iter 30 value 619.801324
## iter 40 value 507.371582
## iter 50 value 383.606302
## iter 60 value 305.571781
## iter 70 value 261.754083
## iter 80 value 230.270573
## iter 90 value 199.153503
## iter 100 value 183.251919
## iter 110 value 170.155944
## iter 120 value 160.549910
## iter 130 value 151.955758
## iter 140 value 142.364687
## iter 150 value 131.690607
## iter 160 value 126.327951
## iter 170 value 121.897241
## iter 180 value 118.442321
## iter 190 value 113.655079
## iter 200 value 107.127937
## iter 210 value 101.169381
## iter 220 value 94.170370
## iter 230 value 89.445134
## iter 240 value 86.063176
## iter 250 value 83.819712
## iter 260 value 81.852320
## iter 270 value 79.693812
## iter 280 value 78.112753
## iter 290 value 76.116949
## iter 300 value 73.833694
## iter 310 value 71.926612
## iter 320 value 69.963229
## iter 330 value 68.019065
## iter 340 value 66.204908
## iter 350 value 63.593963
## iter 360 value 61.011151
## iter 370 value 58.862093
## iter 380 value 55.888522
## iter 390 value 53.943839
## iter 400 value 52.251507
## iter 410 value 51.348918
## iter 420 value 50.005017
## iter 430 value 48.091941
## iter 440 value 46.922592
## iter 450 value 46.164040
## iter 460 value 45.715342
## iter 470 value 45.412681
## iter 480 value 45.125349
## iter 490 value 44.987267
## iter 500 value 44.956544
## final value 44.956544
## stopped after 500 iterations
## # weights: 25
## initial value 1401352.810833
## iter 10 value 22584.803653
## iter 20 value 12571.827010
## iter 30 value 10999.636648
## iter 40 value 9299.545654
## iter 50 value 8684.218053
## iter 60 value 8546.295431
## iter 70 value 8479.953727
## final value 8479.420862
## converged
## # weights: 61
## initial value 1390331.906384
## iter 10 value 32521.360425
## iter 20 value 15876.235844
## iter 30 value 8350.475469
## iter 40 value 4937.028221
## iter 50 value 4263.014694
## iter 60 value 3876.746316
## iter 70 value 3580.612860
## iter 80 value 3502.297249
## iter 90 value 3441.276382
## iter 100 value 3330.367434
## iter 110 value 2999.424938
## iter 120 value 2562.424333
## iter 130 value 2414.360468
## iter 140 value 2246.654949
## iter 150 value 2124.622361
## iter 160 value 1926.190230
## iter 170 value 1834.007432
## iter 180 value 1774.641306
## iter 190 value 1761.455676
## iter 200 value 1757.333466
## iter 210 value 1757.055421
## iter 220 value 1756.354926
## iter 230 value 1751.533501
## iter 240 value 1751.212528
## iter 250 value 1751.068019
## iter 260 value 1750.968093
## iter 270 value 1750.009293
## iter 280 value 1748.692068
## iter 290 value 1747.650787
## iter 300 value 1747.302558
## iter 310 value 1746.756220
## iter 320 value 1745.150726
## iter 330 value 1744.558327
## iter 340 value 1744.449313
## iter 350 value 1744.253696
## iter 360 value 1744.233701
## iter 370 value 1744.205713
## iter 380 value 1744.198107
## iter 390 value 1744.177084
## iter 400 value 1744.001399
## iter 410 value 1743.963266
## iter 420 value 1743.833483
## iter 430 value 1743.515871
## iter 440 value 1742.289992
## iter 450 value 1724.985480
## iter 460 value 1670.950687
## iter 470 value 1648.410435
## iter 480 value 1645.706704
## iter 490 value 1643.629749
## iter 500 value 1639.814294
## final value 1639.814294
## stopped after 500 iterations
## # weights: 121
## initial value 1372741.224659
## iter 10 value 1361.814825
## iter 20 value 828.234699
## iter 30 value 693.247544
## iter 40 value 603.718984
## iter 50 value 541.420420
## iter 60 value 463.671061
## iter 70 value 416.380510
## iter 80 value 386.960445
## iter 90 value 365.384481
## iter 100 value 352.246981
## iter 110 value 337.449557
## iter 120 value 325.041891
## iter 130 value 306.198126
## iter 140 value 291.685324
## iter 150 value 284.360799
## iter 160 value 274.622291
## iter 170 value 269.009801
## iter 180 value 264.440117
## iter 190 value 261.392059
## iter 200 value 259.624470
## iter 210 value 256.351135
## iter 220 value 252.850472
## iter 230 value 250.261778
## iter 240 value 247.486243
## iter 250 value 246.396741
## iter 260 value 246.035204
## iter 270 value 245.002431
## iter 280 value 243.260762
## iter 290 value 239.471442
## iter 300 value 234.747539
## iter 310 value 230.215330
## iter 320 value 224.970118
## iter 330 value 219.633944
## iter 340 value 214.547526
## iter 350 value 210.370115
## iter 360 value 206.715096
## iter 370 value 204.697407
## iter 380 value 202.399961
## iter 390 value 200.251561
## iter 400 value 198.495495
## iter 410 value 196.873847
## iter 420 value 196.155872
## iter 430 value 195.430962
## iter 440 value 194.930192
## iter 450 value 194.738119
## iter 460 value 194.540000
## iter 470 value 194.452072
## iter 480 value 194.402534
## iter 490 value 194.319312
## iter 500 value 194.315074
## final value 194.315074
## stopped after 500 iterations
## # weights: 181
## initial value 1378444.892063
## iter 10 value 1918.274623
## iter 20 value 716.689974
## iter 30 value 530.492845
## iter 40 value 414.374758
## iter 50 value 323.227494
## iter 60 value 274.052553
## iter 70 value 245.623020
## iter 80 value 220.803052
## iter 90 value 201.943922
## iter 100 value 188.866415
## iter 110 value 179.686226
## iter 120 value 171.599604
## iter 130 value 164.153187
## iter 140 value 160.946160
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## iter 170 value 151.303310
## iter 180 value 148.574257
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## iter 480 value 115.908564
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## final value 114.932059
## stopped after 500 iterations
## # weights: 241
## initial value 1365942.690459
## iter 10 value 1202.714914
## iter 20 value 756.406976
## iter 30 value 640.140190
## iter 40 value 540.410517
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## iter 300 value 60.344949
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## iter 400 value 44.086062
## iter 410 value 43.879743
## iter 420 value 43.703380
## iter 430 value 43.501966
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## iter 460 value 42.988365
## iter 470 value 42.853705
## iter 480 value 42.709676
## iter 490 value 42.654831
## iter 500 value 42.641270
## final value 42.641270
## stopped after 500 iterations
## # weights: 25
## initial value 1369311.602739
## iter 10 value 6820.461951
## iter 20 value 5446.843389
## iter 30 value 5295.517703
## iter 40 value 4906.365573
## iter 50 value 4273.899997
## iter 60 value 3727.361713
## iter 70 value 1766.238133
## iter 80 value 1402.899845
## iter 90 value 1346.814811
## iter 100 value 1328.122741
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## iter 130 value 1282.648408
## iter 140 value 1280.563819
## iter 150 value 1278.920853
## iter 160 value 1278.912377
## final value 1278.911363
## converged
## # weights: 61
## initial value 1377040.387857
## iter 10 value 8571.316210
## iter 20 value 6387.743023
## iter 30 value 5753.483085
## iter 40 value 5440.730456
## iter 50 value 4714.881211
## iter 60 value 3345.592322
## iter 70 value 2573.088092
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## iter 100 value 2258.226956
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## iter 130 value 2176.421395
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## iter 160 value 2100.527564
## iter 170 value 2000.596293
## iter 180 value 1818.544666
## iter 190 value 1789.426629
## iter 200 value 1787.530658
## iter 210 value 1773.212944
## iter 220 value 1760.690107
## iter 230 value 1749.447440
## iter 240 value 1746.319286
## iter 250 value 1745.545835
## iter 260 value 1744.931662
## iter 270 value 1744.452723
## iter 280 value 1743.533941
## iter 290 value 1717.677648
## iter 300 value 1698.646441
## iter 310 value 1652.563573
## iter 320 value 1644.484688
## iter 330 value 1642.039126
## iter 340 value 1622.964985
## iter 350 value 1603.421679
## iter 360 value 1583.012274
## iter 370 value 1563.759331
## iter 380 value 1512.874144
## iter 390 value 1348.064613
## iter 400 value 1019.306111
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## iter 460 value 865.222298
## iter 470 value 854.609702
## iter 480 value 848.468739
## iter 490 value 848.260851
## iter 500 value 844.915794
## final value 844.915794
## stopped after 500 iterations
## # weights: 121
## initial value 1426097.473036
## iter 10 value 3019.134982
## iter 20 value 1534.369030
## iter 30 value 1048.182910
## iter 40 value 762.735738
## iter 50 value 650.644900
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## iter 470 value 264.470707
## iter 480 value 264.101405
## iter 490 value 263.905791
## iter 500 value 263.901083
## final value 263.901083
## stopped after 500 iterations
## # weights: 181
## initial value 1386961.737673
## iter 10 value 1078.049854
## iter 20 value 742.927129
## iter 30 value 636.335191
## iter 40 value 520.049415
## iter 50 value 415.406605
## iter 60 value 363.600726
## iter 70 value 322.840754
## iter 80 value 288.649996
## iter 90 value 259.843976
## iter 100 value 241.239006
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## iter 500 value 110.859297
## final value 110.859297
## stopped after 500 iterations
## # weights: 241
## initial value 1330168.016794
## iter 10 value 1354.057588
## iter 20 value 735.288960
## iter 30 value 597.233265
## iter 40 value 461.802342
## iter 50 value 359.972501
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## iter 70 value 242.612727
## iter 80 value 209.506249
## iter 90 value 186.474628
## iter 100 value 169.072803
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## iter 310 value 41.025847
## iter 320 value 38.058901
## iter 330 value 35.478419
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## iter 370 value 27.351663
## iter 380 value 25.782100
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## iter 400 value 23.769805
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## iter 420 value 22.891011
## iter 430 value 22.369908
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## iter 460 value 21.338875
## iter 470 value 20.627857
## iter 480 value 20.050029
## iter 490 value 19.814863
## iter 500 value 19.753910
## final value 19.753910
## stopped after 500 iterations
## # weights: 25
## initial value 1356805.638731
## iter 10 value 75264.400144
## iter 20 value 27260.137525
## iter 30 value 10308.660240
## iter 40 value 7848.320484
## iter 50 value 6828.745253
## iter 60 value 5670.820930
## iter 70 value 4079.676668
## iter 80 value 3184.411883
## iter 90 value 2451.642550
## iter 100 value 1872.174980
## iter 110 value 1696.033481
## iter 120 value 1660.900048
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## iter 140 value 1573.871316
## iter 150 value 1482.148184
## iter 160 value 1478.769317
## iter 170 value 1478.278132
## iter 180 value 1477.975241
## iter 190 value 1476.658226
## final value 1476.608604
## converged
## # weights: 61
## initial value 1366095.037300
## iter 10 value 15500.230585
## iter 20 value 6932.150124
## iter 30 value 5246.628694
## iter 40 value 4242.189076
## iter 50 value 3329.164679
## iter 60 value 2366.227537
## iter 70 value 1708.938104
## iter 80 value 1391.636327
## iter 90 value 1232.280619
## iter 100 value 1118.446332
## iter 110 value 1055.573254
## iter 120 value 1039.652446
## iter 130 value 1029.778817
## iter 140 value 1013.329635
## iter 150 value 965.409263
## iter 160 value 930.003816
## iter 170 value 898.710952
## iter 180 value 865.355531
## iter 190 value 819.846356
## iter 200 value 782.378552
## iter 210 value 763.745785
## iter 220 value 745.942866
## iter 230 value 735.137048
## iter 240 value 726.138018
## iter 250 value 722.721128
## iter 260 value 721.120291
## iter 270 value 718.594266
## iter 280 value 715.903850
## iter 290 value 714.092672
## iter 300 value 713.335423
## iter 310 value 713.095898
## final value 713.071163
## converged
## # weights: 121
## initial value 1409075.898309
## iter 10 value 1847.817905
## iter 20 value 1068.200220
## iter 30 value 893.294344
## iter 40 value 826.486051
## iter 50 value 762.860846
## iter 60 value 717.960527
## iter 70 value 680.295368
## iter 80 value 639.636964
## iter 90 value 610.556957
## iter 100 value 588.675552
## iter 110 value 574.373950
## iter 120 value 558.432178
## iter 130 value 538.771954
## iter 140 value 525.489361
## iter 150 value 517.690046
## iter 160 value 512.989313
## iter 170 value 506.994262
## iter 180 value 502.948438
## iter 190 value 499.307031
## iter 200 value 497.387117
## iter 210 value 495.900498
## iter 220 value 494.245553
## iter 230 value 492.500804
## iter 240 value 491.115169
## iter 250 value 490.250983
## iter 260 value 489.864965
## iter 270 value 489.276606
## iter 280 value 489.005309
## iter 290 value 488.765986
## iter 300 value 488.709992
## iter 310 value 488.689976
## iter 320 value 488.679460
## iter 330 value 488.673236
## iter 340 value 488.518832
## iter 350 value 488.160426
## iter 360 value 488.092817
## iter 370 value 488.081131
## final value 488.081013
## converged
## # weights: 181
## initial value 1420846.970810
## iter 10 value 1677.569524
## iter 20 value 926.712207
## iter 30 value 722.281281
## iter 40 value 645.123054
## iter 50 value 563.440128
## iter 60 value 529.305134
## iter 70 value 516.971043
## iter 80 value 507.290514
## iter 90 value 496.210461
## iter 100 value 486.665287
## iter 110 value 479.418876
## iter 120 value 474.656296
## iter 130 value 468.756272
## iter 140 value 458.049033
## iter 150 value 448.012822
## iter 160 value 437.367768
## iter 170 value 427.826989
## iter 180 value 421.223473
## iter 190 value 418.060853
## iter 200 value 415.888567
## iter 210 value 411.772442
## iter 220 value 407.892739
## iter 230 value 405.791094
## iter 240 value 403.604615
## iter 250 value 402.023665
## iter 260 value 400.749612
## iter 270 value 399.811389
## iter 280 value 399.011901
## iter 290 value 398.163161
## iter 300 value 397.129480
## iter 310 value 396.220651
## iter 320 value 395.269558
## iter 330 value 394.567892
## iter 340 value 393.845009
## iter 350 value 393.280812
## iter 360 value 392.912667
## iter 370 value 392.607859
## iter 380 value 392.233342
## iter 390 value 391.642458
## iter 400 value 391.226363
## iter 410 value 390.869277
## iter 420 value 390.507116
## iter 430 value 390.241278
## iter 440 value 389.886887
## iter 450 value 388.460695
## iter 460 value 386.689697
## iter 470 value 385.321888
## iter 480 value 383.310046
## iter 490 value 380.175348
## iter 500 value 377.556239
## final value 377.556239
## stopped after 500 iterations
## # weights: 241
## initial value 1440672.319555
## iter 10 value 1623.988543
## iter 20 value 940.362169
## iter 30 value 746.413832
## iter 40 value 640.447421
## iter 50 value 572.005881
## iter 60 value 521.119817
## iter 70 value 488.603134
## iter 80 value 464.232070
## iter 90 value 434.453714
## iter 100 value 417.009765
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## iter 120 value 399.750038
## iter 130 value 393.649568
## iter 140 value 389.200958
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## iter 160 value 382.554259
## iter 170 value 379.918877
## iter 180 value 377.691438
## iter 190 value 374.921612
## iter 200 value 371.379408
## iter 210 value 366.090511
## iter 220 value 363.051108
## iter 230 value 360.440429
## iter 240 value 357.332464
## iter 250 value 354.616328
## iter 260 value 352.067334
## iter 270 value 349.349318
## iter 280 value 345.615171
## iter 290 value 342.425243
## iter 300 value 339.867529
## iter 310 value 338.380519
## iter 320 value 337.175378
## iter 330 value 335.947895
## iter 340 value 334.856446
## iter 350 value 333.704725
## iter 360 value 332.549502
## iter 370 value 332.048847
## iter 380 value 331.756735
## iter 390 value 331.416512
## iter 400 value 330.325096
## iter 410 value 329.015926
## iter 420 value 327.481508
## iter 430 value 326.520416
## iter 440 value 325.972047
## iter 450 value 325.346204
## iter 460 value 324.790732
## iter 470 value 324.278601
## iter 480 value 323.578269
## iter 490 value 323.258868
## iter 500 value 322.740780
## final value 322.740780
## stopped after 500 iterations
## # weights: 25
## initial value 1402172.750021
## iter 10 value 63325.462518
## iter 20 value 17374.865040
## iter 30 value 7851.262685
## iter 40 value 7626.392126
## iter 50 value 6850.267576
## iter 60 value 5566.152060
## iter 70 value 4725.894181
## iter 80 value 3978.485308
## iter 90 value 3969.098548
## iter 100 value 3964.586629
## iter 110 value 3897.822752
## iter 120 value 3875.307228
## iter 130 value 3862.255970
## iter 140 value 3854.882489
## iter 150 value 3510.283214
## iter 160 value 2983.364198
## iter 170 value 2155.151247
## iter 180 value 1733.168315
## iter 190 value 1429.036831
## iter 200 value 1311.264154
## iter 210 value 1303.758465
## iter 220 value 1283.137187
## iter 230 value 1282.673322
## iter 240 value 1213.657112
## iter 250 value 1170.855668
## iter 260 value 1151.766948
## iter 270 value 1147.997427
## iter 280 value 1147.215499
## final value 1147.190345
## converged
## # weights: 61
## initial value 1400054.198890
## iter 10 value 4849.047418
## iter 20 value 1552.762399
## iter 30 value 1225.205040
## iter 40 value 949.977230
## iter 50 value 840.550643
## iter 60 value 793.498010
## iter 70 value 751.506218
## iter 80 value 720.738142
## iter 90 value 696.394507
## iter 100 value 669.001708
## iter 110 value 647.103746
## iter 120 value 628.620741
## iter 130 value 624.584764
## iter 140 value 622.206719
## iter 150 value 617.929340
## iter 160 value 611.620803
## iter 170 value 609.056782
## iter 180 value 595.438423
## iter 190 value 586.192734
## iter 200 value 583.608631
## iter 210 value 581.872718
## iter 220 value 581.102191
## iter 230 value 580.674325
## iter 240 value 580.650733
## iter 250 value 580.603061
## iter 260 value 580.512218
## iter 270 value 579.180102
## iter 280 value 578.353066
## iter 290 value 570.205273
## iter 300 value 565.125652
## iter 310 value 563.558467
## iter 320 value 562.761437
## iter 330 value 562.406119
## iter 340 value 562.302868
## iter 350 value 562.286817
## iter 360 value 562.284577
## iter 370 value 562.277496
## iter 380 value 562.230388
## iter 390 value 562.188212
## iter 400 value 562.174503
## iter 410 value 562.171149
## final value 562.170587
## converged
## # weights: 121
## initial value 1358554.334746
## iter 10 value 1338.043646
## iter 20 value 851.422232
## iter 30 value 709.174966
## iter 40 value 620.502330
## iter 50 value 578.488141
## iter 60 value 533.582756
## iter 70 value 498.647211
## iter 80 value 465.874182
## iter 90 value 436.687549
## iter 100 value 408.599125
## iter 110 value 381.666885
## iter 120 value 360.529755
## iter 130 value 345.929137
## iter 140 value 336.035854
## iter 150 value 323.494814
## iter 160 value 316.865913
## iter 170 value 310.223047
## iter 180 value 304.961407
## iter 190 value 300.516850
## iter 200 value 297.458252
## iter 210 value 295.205302
## iter 220 value 292.894244
## iter 230 value 291.225571
## iter 240 value 290.257770
## iter 250 value 289.800194
## iter 260 value 289.590261
## iter 270 value 289.000019
## iter 280 value 287.961400
## iter 290 value 285.572942
## iter 300 value 279.040888
## iter 310 value 271.858443
## iter 320 value 260.272372
## iter 330 value 250.024629
## iter 340 value 243.181167
## iter 350 value 238.249752
## iter 360 value 235.927820
## iter 370 value 234.576488
## iter 380 value 234.043025
## iter 390 value 232.784801
## iter 400 value 232.382912
## iter 410 value 232.200976
## iter 420 value 232.128865
## iter 430 value 232.041128
## iter 440 value 232.016297
## iter 450 value 232.008511
## iter 460 value 232.006337
## iter 470 value 232.005158
## iter 480 value 232.004205
## final value 232.003724
## converged
## # weights: 181
## initial value 1389840.698867
## iter 10 value 1193.943373
## iter 20 value 774.036560
## iter 30 value 569.949465
## iter 40 value 464.371061
## iter 50 value 381.404003
## iter 60 value 341.305451
## iter 70 value 309.188409
## iter 80 value 270.540829
## iter 90 value 253.057644
## iter 100 value 239.469340
## iter 110 value 228.362532
## iter 120 value 220.170792
## iter 130 value 209.910687
## iter 140 value 195.954325
## iter 150 value 185.858311
## iter 160 value 176.221664
## iter 170 value 169.398743
## iter 180 value 161.459833
## iter 190 value 152.523597
## iter 200 value 145.079997
## iter 210 value 140.144102
## iter 220 value 138.377094
## iter 230 value 136.092874
## iter 240 value 133.733048
## iter 250 value 132.113689
## iter 260 value 131.084706
## iter 270 value 129.388314
## iter 280 value 127.586687
## iter 290 value 125.492254
## iter 300 value 123.634320
## iter 310 value 121.746325
## iter 320 value 119.770700
## iter 330 value 116.800554
## iter 340 value 114.280010
## iter 350 value 112.137141
## iter 360 value 110.864362
## iter 370 value 110.563737
## iter 380 value 110.452383
## iter 390 value 110.264549
## iter 400 value 109.943746
## iter 410 value 109.704432
## iter 420 value 109.357755
## iter 430 value 108.886792
## iter 440 value 108.303051
## iter 450 value 107.464079
## iter 460 value 106.686670
## iter 470 value 105.821288
## iter 480 value 104.881676
## iter 490 value 104.054904
## iter 500 value 102.918903
## final value 102.918903
## stopped after 500 iterations
## # weights: 241
## initial value 1372968.306651
## iter 10 value 1170.623995
## iter 20 value 735.632998
## iter 30 value 632.049007
## iter 40 value 544.407516
## iter 50 value 427.962919
## iter 60 value 342.964965
## iter 70 value 287.459051
## iter 80 value 260.112230
## iter 90 value 234.280996
## iter 100 value 206.523434
## iter 110 value 192.114085
## iter 120 value 179.062349
## iter 130 value 166.527489
## iter 140 value 153.064342
## iter 150 value 142.514745
## iter 160 value 134.331668
## iter 170 value 130.198101
## iter 180 value 125.191962
## iter 190 value 119.377881
## iter 200 value 114.618292
## iter 210 value 109.915254
## iter 220 value 103.605734
## iter 230 value 98.510239
## iter 240 value 94.561267
## iter 250 value 90.567594
## iter 260 value 87.981678
## iter 270 value 86.240400
## iter 280 value 84.617589
## iter 290 value 82.273894
## iter 300 value 80.424550
## iter 310 value 78.110941
## iter 320 value 76.476324
## iter 330 value 74.292248
## iter 340 value 72.280307
## iter 350 value 70.222535
## iter 360 value 68.658065
## iter 370 value 67.480433
## iter 380 value 66.035567
## iter 390 value 64.494497
## iter 400 value 63.446779
## iter 410 value 62.564942
## iter 420 value 62.008232
## iter 430 value 61.570939
## iter 440 value 61.002621
## iter 450 value 59.970512
## iter 460 value 59.154846
## iter 470 value 58.221127
## iter 480 value 57.589656
## iter 490 value 57.435851
## iter 500 value 57.343203
## final value 57.343203
## stopped after 500 iterations
## # weights: 25
## initial value 1430460.437131
## iter 10 value 6192.721907
## iter 20 value 5263.162093
## iter 30 value 5176.136277
## iter 40 value 5168.031669
## iter 50 value 5089.330370
## iter 60 value 4881.674891
## iter 70 value 4454.241373
## iter 80 value 2551.895501
## iter 90 value 1623.266511
## iter 100 value 1403.561932
## iter 110 value 1342.058141
## iter 120 value 1316.912993
## iter 130 value 1294.208753
## iter 140 value 1286.474221
## iter 150 value 1283.094032
## iter 160 value 1281.159469
## iter 170 value 1281.118938
## final value 1281.115241
## converged
## # weights: 61
## initial value 1396403.612522
## iter 10 value 156801.679997
## iter 20 value 9255.601861
## iter 30 value 5820.746535
## iter 40 value 4175.369343
## iter 50 value 2776.615704
## iter 60 value 2341.844432
## iter 70 value 2258.341534
## iter 80 value 2090.884091
## iter 90 value 1610.385517
## iter 100 value 1365.834262
## iter 110 value 1206.686385
## iter 120 value 1007.860471
## iter 130 value 860.712021
## iter 140 value 817.375927
## iter 150 value 780.292554
## iter 160 value 753.563689
## iter 170 value 736.667218
## iter 180 value 721.136633
## iter 190 value 705.856157
## iter 200 value 687.779513
## iter 210 value 676.467965
## iter 220 value 673.194834
## iter 230 value 660.822517
## iter 240 value 647.708209
## iter 250 value 637.743736
## iter 260 value 629.059467
## iter 270 value 625.919490
## iter 280 value 624.196927
## iter 290 value 622.561522
## iter 300 value 620.361731
## iter 310 value 618.560679
## iter 320 value 617.385723
## iter 330 value 616.722429
## iter 340 value 616.714215
## iter 350 value 616.652881
## iter 360 value 616.578315
## iter 370 value 615.665720
## iter 380 value 614.923055
## iter 390 value 614.485093
## iter 400 value 614.195769
## iter 410 value 614.126088
## iter 420 value 614.098984
## iter 430 value 614.085006
## iter 440 value 614.077636
## iter 450 value 614.074287
## final value 614.074150
## converged
## # weights: 121
## initial value 1388293.363406
## iter 10 value 1383.165057
## iter 20 value 885.247952
## iter 30 value 695.660265
## iter 40 value 592.360729
## iter 50 value 530.887238
## iter 60 value 479.486363
## iter 70 value 462.548999
## iter 80 value 451.181957
## iter 90 value 435.353896
## iter 100 value 410.620112
## iter 110 value 394.350099
## iter 120 value 385.153318
## iter 130 value 381.087108
## iter 140 value 378.335145
## iter 150 value 372.178297
## iter 160 value 364.028707
## iter 170 value 356.849707
## iter 180 value 348.375675
## iter 190 value 341.628982
## iter 200 value 338.214983
## iter 210 value 330.911257
## iter 220 value 317.532809
## iter 230 value 311.188692
## iter 240 value 301.231592
## iter 250 value 296.364708
## iter 260 value 294.794725
## iter 270 value 290.425789
## iter 280 value 285.353601
## iter 290 value 278.480588
## iter 300 value 272.256375
## iter 310 value 263.844181
## iter 320 value 259.584139
## iter 330 value 255.130565
## iter 340 value 249.075407
## iter 350 value 246.161343
## iter 360 value 242.799917
## iter 370 value 241.242062
## iter 380 value 240.429648
## iter 390 value 240.284215
## iter 400 value 240.150351
## iter 410 value 239.883590
## iter 420 value 239.669712
## iter 430 value 239.613921
## iter 440 value 239.522029
## iter 450 value 239.283229
## iter 460 value 238.991782
## iter 470 value 238.453257
## iter 480 value 237.994992
## iter 490 value 237.738315
## iter 500 value 237.651468
## final value 237.651468
## stopped after 500 iterations
## # weights: 181
## initial value 1359360.663325
## iter 10 value 1401.921210
## iter 20 value 875.177878
## iter 30 value 656.996690
## iter 40 value 518.820492
## iter 50 value 462.048776
## iter 60 value 405.161586
## iter 70 value 339.862593
## iter 80 value 301.241940
## iter 90 value 274.229864
## iter 100 value 254.383086
## iter 110 value 240.361910
## iter 120 value 226.174053
## iter 130 value 218.524045
## iter 140 value 211.111749
## iter 150 value 201.036837
## iter 160 value 189.585912
## iter 170 value 180.469228
## iter 180 value 170.126855
## iter 190 value 157.838030
## iter 200 value 149.298143
## iter 210 value 143.991844
## iter 220 value 140.057437
## iter 230 value 137.532127
## iter 240 value 136.269383
## iter 250 value 135.180173
## iter 260 value 134.072039
## iter 270 value 132.887361
## iter 280 value 130.873392
## iter 290 value 129.559743
## iter 300 value 128.438907
## iter 310 value 127.581425
## iter 320 value 127.098398
## iter 330 value 126.701337
## iter 340 value 126.482498
## iter 350 value 126.177734
## iter 360 value 125.963367
## iter 370 value 125.891295
## iter 380 value 125.857253
## iter 390 value 125.788713
## iter 400 value 125.710703
## iter 410 value 125.571493
## iter 420 value 125.439709
## iter 430 value 125.125151
## iter 440 value 124.694451
## iter 450 value 124.137828
## iter 460 value 123.722588
## iter 470 value 123.426111
## iter 480 value 123.152354
## iter 490 value 122.335544
## iter 500 value 121.385912
## final value 121.385912
## stopped after 500 iterations
## # weights: 241
## initial value 1339079.578028
## iter 10 value 2295.304643
## iter 20 value 942.311683
## iter 30 value 713.771595
## iter 40 value 566.067968
## iter 50 value 490.033781
## iter 60 value 430.538978
## iter 70 value 362.603381
## iter 80 value 319.972221
## iter 90 value 293.257829
## iter 100 value 270.366671
## iter 110 value 250.868414
## iter 120 value 232.745818
## iter 130 value 219.992035
## iter 140 value 211.611574
## iter 150 value 201.267775
## iter 160 value 192.145775
## iter 170 value 177.589026
## iter 180 value 166.659839
## iter 190 value 156.404584
## iter 200 value 150.844173
## iter 210 value 145.291709
## iter 220 value 139.904840
## iter 230 value 133.461468
## iter 240 value 128.691452
## iter 250 value 124.241918
## iter 260 value 120.144342
## iter 270 value 115.950384
## iter 280 value 112.089584
## iter 290 value 107.784777
## iter 300 value 103.943651
## iter 310 value 98.856854
## iter 320 value 94.423971
## iter 330 value 89.956071
## iter 340 value 85.707613
## iter 350 value 82.353643
## iter 360 value 79.497769
## iter 370 value 76.804721
## iter 380 value 74.571519
## iter 390 value 73.214677
## iter 400 value 71.836160
## iter 410 value 70.747803
## iter 420 value 69.745380
## iter 430 value 69.151290
## iter 440 value 68.726440
## iter 450 value 68.170816
## iter 460 value 67.481096
## iter 470 value 66.590129
## iter 480 value 65.743445
## iter 490 value 65.329004
## iter 500 value 65.273394
## final value 65.273394
## stopped after 500 iterations
## # weights: 25
## initial value 1371306.977207
## iter 10 value 6163.431950
## iter 20 value 5740.578549
## iter 30 value 5724.250739
## iter 40 value 5703.430395
## iter 50 value 5673.624462
## final value 5673.568283
## converged
## # weights: 61
## initial value 1356619.584693
## iter 10 value 2315.114235
## iter 20 value 1665.102298
## iter 30 value 1462.016843
## iter 40 value 1206.160009
## iter 50 value 1086.399977
## iter 60 value 982.460133
## iter 70 value 866.940522
## iter 80 value 773.655021
## iter 90 value 743.286615
## iter 100 value 725.367140
## iter 110 value 716.357390
## iter 120 value 701.828081
## iter 130 value 689.110762
## iter 140 value 684.861526
## iter 150 value 682.639225
## iter 160 value 679.578819
## iter 170 value 677.936305
## iter 180 value 675.877074
## iter 190 value 675.245495
## iter 200 value 675.151190
## iter 210 value 675.003065
## iter 220 value 674.894572
## iter 230 value 674.888210
## iter 240 value 674.878981
## iter 250 value 674.849118
## iter 260 value 674.813656
## iter 270 value 674.759781
## iter 280 value 674.552664
## iter 290 value 674.354239
## iter 300 value 674.115244
## iter 310 value 673.920097
## iter 320 value 673.882352
## iter 330 value 673.869954
## final value 673.829711
## converged
## # weights: 121
## initial value 1414504.383448
## iter 10 value 1277.136590
## iter 20 value 856.073759
## iter 30 value 696.579075
## iter 40 value 603.528958
## iter 50 value 527.646195
## iter 60 value 450.848424
## iter 70 value 418.231172
## iter 80 value 399.746059
## iter 90 value 388.031543
## iter 100 value 378.898658
## iter 110 value 372.751192
## iter 120 value 363.951122
## iter 130 value 349.706944
## iter 140 value 337.203507
## iter 150 value 327.520597
## iter 160 value 319.423250
## iter 170 value 314.575674
## iter 180 value 311.274687
## iter 190 value 307.789178
## iter 200 value 305.134440
## iter 210 value 303.450601
## iter 220 value 301.233392
## iter 230 value 298.619777
## iter 240 value 296.963919
## iter 250 value 296.434565
## iter 260 value 296.220354
## iter 270 value 295.976852
## iter 280 value 295.180732
## iter 290 value 294.270332
## iter 300 value 293.856354
## iter 310 value 293.733665
## iter 320 value 293.170375
## iter 330 value 290.299040
## iter 340 value 288.018065
## iter 350 value 285.239615
## iter 360 value 280.105272
## iter 370 value 277.792216
## iter 380 value 271.443322
## iter 390 value 267.847396
## iter 400 value 263.243480
## iter 410 value 262.496018
## iter 420 value 261.605023
## iter 430 value 261.354269
## iter 440 value 261.286821
## iter 450 value 261.226228
## iter 460 value 261.127786
## iter 470 value 261.041762
## iter 480 value 261.004344
## iter 490 value 260.896863
## iter 500 value 260.877307
## final value 260.877307
## stopped after 500 iterations
## # weights: 181
## initial value 1386840.610073
## iter 10 value 1110.872717
## iter 20 value 772.085557
## iter 30 value 628.226471
## iter 40 value 460.964891
## iter 50 value 383.573460
## iter 60 value 341.633726
## iter 70 value 309.692626
## iter 80 value 263.910697
## iter 90 value 233.178115
## iter 100 value 219.739706
## iter 110 value 208.650735
## iter 120 value 196.330145
## iter 130 value 186.609681
## iter 140 value 174.323692
## iter 150 value 159.330326
## iter 160 value 148.764315
## iter 170 value 141.767555
## iter 180 value 134.696091
## iter 190 value 128.460008
## iter 200 value 122.750652
## iter 210 value 118.479390
## iter 220 value 115.432883
## iter 230 value 113.302326
## iter 240 value 111.177109
## iter 250 value 109.010053
## iter 260 value 107.737632
## iter 270 value 106.878072
## iter 280 value 105.427370
## iter 290 value 104.184649
## iter 300 value 103.097125
## iter 310 value 102.170722
## iter 320 value 101.641580
## iter 330 value 101.313430
## iter 340 value 100.576313
## iter 350 value 99.653474
## iter 360 value 99.002302
## iter 370 value 98.713766
## iter 380 value 98.596451
## iter 390 value 98.435520
## iter 400 value 98.137061
## iter 410 value 97.927819
## iter 420 value 97.356260
## iter 430 value 96.681623
## iter 440 value 96.220542
## iter 450 value 95.894381
## iter 460 value 95.586668
## iter 470 value 95.245946
## iter 480 value 94.586872
## iter 490 value 94.017195
## iter 500 value 93.008983
## final value 93.008983
## stopped after 500 iterations
## # weights: 241
## initial value 1399979.248215
## iter 10 value 3258.873150
## iter 20 value 947.290203
## iter 30 value 665.382210
## iter 40 value 473.081693
## iter 50 value 374.643924
## iter 60 value 318.499178
## iter 70 value 276.049849
## iter 80 value 236.073737
## iter 90 value 207.059490
## iter 100 value 173.152816
## iter 110 value 136.497582
## iter 120 value 118.893924
## iter 130 value 106.574788
## iter 140 value 96.096820
## iter 150 value 89.034156
## iter 160 value 81.835465
## iter 170 value 76.953343
## iter 180 value 70.811236
## iter 190 value 65.132480
## iter 200 value 60.455958
## iter 210 value 56.354792
## iter 220 value 52.486682
## iter 230 value 49.864610
## iter 240 value 47.988067
## iter 250 value 46.385572
## iter 260 value 43.862486
## iter 270 value 42.161818
## iter 280 value 40.899606
## iter 290 value 39.534512
## iter 300 value 38.297851
## iter 310 value 37.051015
## iter 320 value 35.446105
## iter 330 value 34.168357
## iter 340 value 33.033579
## iter 350 value 31.951122
## iter 360 value 30.829325
## iter 370 value 29.631059
## iter 380 value 28.717831
## iter 390 value 27.589415
## iter 400 value 26.797909
## iter 410 value 26.194780
## iter 420 value 25.574603
## iter 430 value 24.900634
## iter 440 value 24.466159
## iter 450 value 24.066124
## iter 460 value 23.693180
## iter 470 value 23.432414
## iter 480 value 23.227532
## iter 490 value 23.116645
## iter 500 value 23.082550
## final value 23.082550
## stopped after 500 iterations
## # weights: 25
## initial value 1396719.500964
## iter 10 value 14442.509209
## iter 20 value 8318.877569
## iter 30 value 2449.613836
## iter 40 value 1442.390049
## iter 50 value 1264.974398
## iter 60 value 1229.307221
## iter 70 value 1192.892924
## iter 80 value 1180.448678
## iter 90 value 1176.394756
## iter 100 value 1172.649592
## iter 110 value 1151.043775
## iter 120 value 1139.618268
## iter 130 value 1137.936695
## iter 140 value 1135.762766
## iter 150 value 1135.621121
## iter 160 value 1135.604907
## iter 170 value 1135.571068
## iter 180 value 1135.545407
## final value 1135.531521
## converged
## # weights: 61
## initial value 1395559.158788
## iter 10 value 2965.362259
## iter 20 value 1648.111629
## iter 30 value 1078.679235
## iter 40 value 858.968683
## iter 50 value 763.990663
## iter 60 value 728.855353
## iter 70 value 688.317954
## iter 80 value 659.053913
## iter 90 value 640.301413
## iter 100 value 622.044528
## iter 110 value 610.951452
## iter 120 value 600.766610
## iter 130 value 595.506993
## iter 140 value 593.075413
## iter 150 value 588.107703
## iter 160 value 579.301858
## iter 170 value 571.500934
## iter 180 value 565.002137
## iter 190 value 563.150493
## iter 200 value 561.290695
## iter 210 value 560.476340
## iter 220 value 560.281806
## iter 230 value 560.173274
## iter 240 value 560.024207
## iter 250 value 559.949920
## iter 260 value 559.567576
## iter 270 value 558.756010
## iter 280 value 558.186569
## iter 290 value 556.313216
## iter 300 value 555.890766
## iter 310 value 555.799140
## iter 320 value 555.623466
## iter 330 value 555.556972
## iter 340 value 555.511387
## iter 350 value 555.476564
## iter 360 value 555.468100
## iter 360 value 555.468096
## iter 360 value 555.468093
## final value 555.468093
## converged
## # weights: 121
## initial value 1367573.323344
## iter 10 value 1387.922761
## iter 20 value 902.438461
## iter 30 value 741.281949
## iter 40 value 620.089931
## iter 50 value 553.786006
## iter 60 value 507.241444
## iter 70 value 462.427228
## iter 80 value 433.559924
## iter 90 value 413.686746
## iter 100 value 395.764417
## iter 110 value 381.836097
## iter 120 value 374.803124
## iter 130 value 366.707894
## iter 140 value 362.038174
## iter 150 value 355.314352
## iter 160 value 349.264977
## iter 170 value 343.676292
## iter 180 value 337.771236
## iter 190 value 333.605960
## iter 200 value 329.821507
## iter 210 value 324.452005
## iter 220 value 320.154650
## iter 230 value 317.146476
## iter 240 value 315.115750
## iter 250 value 314.159772
## iter 260 value 313.338361
## iter 270 value 312.229241
## iter 280 value 309.663396
## iter 290 value 306.046459
## iter 300 value 302.279587
## iter 310 value 298.687355
## iter 320 value 294.957058
## iter 330 value 291.051472
## iter 340 value 289.002688
## iter 350 value 285.647698
## iter 360 value 282.719438
## iter 370 value 280.046920
## iter 380 value 277.528513
## iter 390 value 274.400966
## iter 400 value 271.914406
## iter 410 value 269.263666
## iter 420 value 266.516046
## iter 430 value 265.142300
## iter 440 value 263.885987
## iter 450 value 262.816387
## iter 460 value 261.020055
## iter 470 value 258.699086
## iter 480 value 255.618241
## iter 490 value 253.725632
## iter 500 value 253.034260
## final value 253.034260
## stopped after 500 iterations
## # weights: 181
## initial value 1376459.602667
## iter 10 value 1127.345852
## iter 20 value 832.945479
## iter 30 value 674.005026
## iter 40 value 583.374431
## iter 50 value 505.273381
## iter 60 value 469.719277
## iter 70 value 435.532127
## iter 80 value 389.899075
## iter 90 value 352.911699
## iter 100 value 323.992758
## iter 110 value 298.214756
## iter 120 value 274.799992
## iter 130 value 260.649590
## iter 140 value 248.773487
## iter 150 value 230.974150
## iter 160 value 213.545800
## iter 170 value 204.372007
## iter 180 value 198.059819
## iter 190 value 192.314134
## iter 200 value 185.412410
## iter 210 value 177.952262
## iter 220 value 170.755753
## iter 230 value 166.903620
## iter 240 value 163.051089
## iter 250 value 160.017337
## iter 260 value 157.171586
## iter 270 value 154.385850
## iter 280 value 152.290162
## iter 290 value 149.698070
## iter 300 value 147.543353
## iter 310 value 143.980594
## iter 320 value 141.781824
## iter 330 value 139.406858
## iter 340 value 136.832988
## iter 350 value 135.208600
## iter 360 value 134.298948
## iter 370 value 133.566552
## iter 380 value 133.280880
## iter 390 value 132.588687
## iter 400 value 131.662410
## iter 410 value 130.859474
## iter 420 value 129.970304
## iter 430 value 128.990268
## iter 440 value 127.352780
## iter 450 value 126.228489
## iter 460 value 123.407267
## iter 470 value 120.238405
## iter 480 value 118.793030
## iter 490 value 117.143551
## iter 500 value 113.849053
## final value 113.849053
## stopped after 500 iterations
## # weights: 241
## initial value 1389790.278838
## iter 10 value 1263.565629
## iter 20 value 793.948900
## iter 30 value 632.273659
## iter 40 value 520.049714
## iter 50 value 407.226424
## iter 60 value 347.868354
## iter 70 value 310.508556
## iter 80 value 279.572911
## iter 90 value 246.255188
## iter 100 value 212.968424
## iter 110 value 187.778570
## iter 120 value 169.578705
## iter 130 value 159.193569
## iter 140 value 151.159279
## iter 150 value 142.326458
## iter 160 value 134.003837
## iter 170 value 123.380027
## iter 180 value 116.739004
## iter 190 value 109.334043
## iter 200 value 104.365390
## iter 210 value 100.136316
## iter 220 value 97.064740
## iter 230 value 93.927804
## iter 240 value 88.875125
## iter 250 value 86.337503
## iter 260 value 83.020601
## iter 270 value 80.476937
## iter 280 value 78.064533
## iter 290 value 76.258054
## iter 300 value 74.680371
## iter 310 value 73.122328
## iter 320 value 71.469310
## iter 330 value 69.455467
## iter 340 value 67.549627
## iter 350 value 65.934425
## iter 360 value 64.218492
## iter 370 value 62.305616
## iter 380 value 60.605498
## iter 390 value 59.204247
## iter 400 value 57.526085
## iter 410 value 55.349930
## iter 420 value 53.616819
## iter 430 value 52.351348
## iter 440 value 51.393803
## iter 450 value 50.241243
## iter 460 value 48.883759
## iter 470 value 47.611422
## iter 480 value 46.592423
## iter 490 value 46.342161
## iter 500 value 46.248030
## final value 46.248030
## stopped after 500 iterations
## # weights: 25
## initial value 1386822.515707
## iter 10 value 15524.497983
## iter 20 value 13787.875760
## iter 30 value 5598.753686
## iter 40 value 4652.935684
## iter 50 value 3910.788261
## iter 60 value 2928.643200
## iter 70 value 2458.895845
## iter 80 value 1769.742705
## iter 90 value 1546.110268
## iter 100 value 1416.175404
## iter 110 value 1301.132530
## iter 120 value 1258.077407
## iter 130 value 1206.200747
## iter 140 value 1157.611149
## iter 150 value 1135.895858
## iter 160 value 1133.170304
## iter 170 value 1132.384037
## iter 180 value 1131.459750
## final value 1131.435314
## converged
## # weights: 61
## initial value 1364693.216837
## iter 10 value 14839.183375
## iter 20 value 2275.262623
## iter 30 value 1670.628631
## iter 40 value 1321.568565
## iter 50 value 1154.518087
## iter 60 value 1087.910583
## iter 70 value 1018.805567
## iter 80 value 989.827995
## iter 90 value 960.107556
## iter 100 value 928.462403
## iter 110 value 888.292781
## iter 120 value 859.054365
## iter 130 value 846.846802
## iter 140 value 838.287877
## iter 150 value 816.122115
## iter 160 value 792.460452
## iter 170 value 785.912732
## iter 180 value 782.657998
## iter 190 value 780.667595
## iter 200 value 779.275156
## iter 210 value 779.111645
## iter 220 value 779.036366
## iter 230 value 778.989731
## iter 240 value 778.049105
## iter 250 value 772.677945
## iter 260 value 765.026100
## iter 270 value 760.684713
## iter 280 value 760.062316
## iter 290 value 760.053246
## final value 760.053201
## converged
## # weights: 121
## initial value 1380308.695246
## iter 10 value 1941.494081
## iter 20 value 1195.631407
## iter 30 value 1019.595749
## iter 40 value 885.958336
## iter 50 value 815.992857
## iter 60 value 763.370679
## iter 70 value 732.576310
## iter 80 value 711.444962
## iter 90 value 696.220971
## iter 100 value 677.345606
## iter 110 value 664.857428
## iter 120 value 650.971994
## iter 130 value 639.772116
## iter 140 value 628.478801
## iter 150 value 618.955114
## iter 160 value 610.799521
## iter 170 value 606.893924
## iter 180 value 601.448907
## iter 190 value 594.374696
## iter 200 value 589.413595
## iter 210 value 581.873617
## iter 220 value 576.469197
## iter 230 value 572.721473
## iter 240 value 570.470259
## iter 250 value 569.720771
## iter 260 value 568.911611
## iter 270 value 567.781883
## iter 280 value 566.993064
## iter 290 value 566.179292
## iter 300 value 565.794904
## iter 310 value 565.718524
## iter 320 value 565.697809
## iter 330 value 565.695020
## iter 340 value 565.694307
## iter 340 value 565.694306
## iter 340 value 565.694306
## final value 565.694306
## converged
## # weights: 181
## initial value 1374836.899352
## iter 10 value 1128.775356
## iter 20 value 844.420908
## iter 30 value 732.954771
## iter 40 value 640.927126
## iter 50 value 587.094388
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## iter 480 value 366.302104
## iter 490 value 365.517845
## iter 500 value 365.259951
## final value 365.259951
## stopped after 500 iterations
## # weights: 241
## initial value 1433429.552750
## iter 10 value 1529.795428
## iter 20 value 965.684454
## iter 30 value 790.941457
## iter 40 value 653.460221
## iter 50 value 585.006296
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## iter 320 value 363.922714
## iter 330 value 361.745430
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## iter 400 value 352.003418
## iter 410 value 350.450055
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## iter 470 value 344.155835
## iter 480 value 343.317025
## iter 490 value 342.881579
## iter 500 value 342.563643
## final value 342.563643
## stopped after 500 iterations
## # weights: 25
## initial value 1386845.223632
## iter 10 value 6249.655546
## iter 20 value 5381.032915
## iter 30 value 5365.504934
## iter 40 value 5360.341599
## iter 50 value 5316.480930
## iter 60 value 5200.182221
## iter 70 value 4867.016027
## iter 80 value 4245.792816
## iter 90 value 3908.976463
## iter 100 value 3576.700343
## iter 110 value 2962.377398
## iter 120 value 1966.052646
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## iter 140 value 1394.010661
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## iter 210 value 1247.815759
## iter 220 value 1246.132893
## iter 230 value 1245.142563
## iter 240 value 1244.923777
## final value 1244.923341
## converged
## # weights: 61
## initial value 1398990.279805
## iter 10 value 300265.287882
## iter 20 value 17594.371402
## iter 30 value 10314.928058
## iter 40 value 6158.556648
## iter 50 value 4059.392208
## iter 60 value 2370.793201
## iter 70 value 1318.551290
## iter 80 value 1057.868321
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## iter 200 value 726.693376
## iter 210 value 715.405384
## iter 220 value 712.459048
## iter 230 value 706.752590
## iter 240 value 695.826603
## iter 250 value 693.557235
## iter 260 value 693.403977
## iter 270 value 692.376719
## iter 280 value 689.528007
## iter 290 value 682.595336
## iter 300 value 669.161832
## iter 310 value 666.248076
## iter 320 value 664.874063
## iter 330 value 664.814038
## iter 340 value 664.347113
## iter 350 value 662.075452
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## iter 400 value 656.442250
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## iter 420 value 653.285933
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## iter 470 value 645.048570
## iter 480 value 644.601991
## iter 490 value 642.344036
## iter 500 value 642.079762
## final value 642.079762
## stopped after 500 iterations
## # weights: 121
## initial value 1294845.404213
## iter 10 value 3144.129144
## iter 20 value 1372.187645
## iter 30 value 887.961238
## iter 40 value 677.177523
## iter 50 value 589.167057
## iter 60 value 540.391271
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## iter 290 value 323.855979
## iter 300 value 320.509499
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## iter 330 value 313.449930
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## iter 500 value 294.984937
## final value 294.984937
## stopped after 500 iterations
## # weights: 181
## initial value 1398210.132030
## iter 10 value 1212.487486
## iter 20 value 790.755675
## iter 30 value 599.559610
## iter 40 value 461.524122
## iter 50 value 376.471882
## iter 60 value 328.070187
## iter 70 value 291.768074
## iter 80 value 251.614135
## iter 90 value 231.770735
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## iter 480 value 118.671012
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## iter 500 value 118.237661
## final value 118.237661
## stopped after 500 iterations
## # weights: 241
## initial value 1459404.078220
## iter 10 value 1487.519872
## iter 20 value 857.221851
## iter 30 value 644.349686
## iter 40 value 466.824808
## iter 50 value 357.772342
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## iter 100 value 172.037279
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## iter 170 value 117.533077
## iter 180 value 113.474639
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## iter 300 value 77.594670
## iter 310 value 76.698831
## iter 320 value 76.041420
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## iter 340 value 74.662818
## iter 350 value 74.189894
## iter 360 value 73.649097
## iter 370 value 73.076135
## iter 380 value 72.587036
## iter 390 value 71.832190
## iter 400 value 71.344550
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## iter 420 value 70.545764
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## iter 480 value 67.682656
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## final value 67.382174
## stopped after 500 iterations
## # weights: 25
## initial value 1348348.565099
## iter 10 value 16499.206246
## iter 20 value 16499.163274
## iter 20 value 16499.163123
## iter 20 value 16499.163094
## final value 16499.163094
## converged
## # weights: 61
## initial value 1396640.513174
## iter 10 value 4718.844536
## iter 20 value 3443.463331
## iter 30 value 2533.873000
## iter 40 value 2086.752691
## iter 50 value 1607.773781
## iter 60 value 1101.315221
## iter 70 value 1053.582764
## iter 80 value 1018.449464
## iter 90 value 969.875948
## iter 100 value 891.287658
## iter 110 value 868.836523
## iter 120 value 852.234276
## iter 130 value 843.198578
## iter 140 value 840.460822
## iter 150 value 840.226028
## iter 160 value 838.442078
## iter 170 value 831.110618
## iter 180 value 817.603416
## iter 190 value 813.687248
## iter 200 value 813.233226
## iter 210 value 810.633795
## iter 220 value 808.779332
## iter 230 value 805.719698
## iter 240 value 799.319172
## iter 250 value 797.687126
## iter 260 value 797.667286
## iter 270 value 797.389020
## iter 280 value 795.136071
## iter 290 value 794.254383
## iter 300 value 790.458049
## iter 310 value 781.969177
## iter 320 value 775.534262
## iter 330 value 769.243791
## iter 340 value 766.209801
## iter 350 value 759.672119
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## iter 370 value 733.563384
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## iter 390 value 725.409249
## iter 400 value 711.628712
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## iter 470 value 660.951895
## iter 480 value 652.179496
## iter 490 value 648.466928
## iter 500 value 637.004503
## final value 637.004503
## stopped after 500 iterations
## # weights: 121
## initial value 1348256.353507
## iter 10 value 1481.244996
## iter 20 value 889.570108
## iter 30 value 723.199006
## iter 40 value 642.364368
## iter 50 value 584.535771
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## iter 70 value 469.968395
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## iter 220 value 317.729843
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## iter 470 value 277.096578
## iter 480 value 276.908288
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## iter 500 value 276.705110
## final value 276.705110
## stopped after 500 iterations
## # weights: 181
## initial value 1445155.038464
## iter 10 value 1610.565559
## iter 20 value 861.819704
## iter 30 value 609.107140
## iter 40 value 522.382345
## iter 50 value 430.293145
## iter 60 value 338.884494
## iter 70 value 308.797613
## iter 80 value 285.552644
## iter 90 value 271.115860
## iter 100 value 261.662764
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## iter 470 value 152.566719
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## iter 490 value 151.122864
## iter 500 value 150.331236
## final value 150.331236
## stopped after 500 iterations
## # weights: 241
## initial value 1409936.924068
## iter 10 value 1073.645391
## iter 20 value 798.573291
## iter 30 value 673.105183
## iter 40 value 529.072443
## iter 50 value 435.605381
## iter 60 value 364.982799
## iter 70 value 331.076917
## iter 80 value 297.309189
## iter 90 value 255.045402
## iter 100 value 223.517569
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## iter 130 value 160.921614
## iter 140 value 148.956390
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## iter 180 value 106.557323
## iter 190 value 95.712099
## iter 200 value 89.172185
## iter 210 value 85.081968
## iter 220 value 79.018241
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## iter 240 value 70.957451
## iter 250 value 67.435565
## iter 260 value 64.894122
## iter 270 value 62.176107
## iter 280 value 58.528375
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## iter 300 value 52.464610
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## iter 320 value 49.685870
## iter 330 value 47.981879
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## iter 350 value 44.838476
## iter 360 value 43.844011
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## iter 380 value 41.750138
## iter 390 value 41.061854
## iter 400 value 40.602270
## iter 410 value 40.297357
## iter 420 value 40.065167
## iter 430 value 39.772729
## iter 440 value 39.537431
## iter 450 value 39.326951
## iter 460 value 39.107489
## iter 470 value 38.924041
## iter 480 value 38.741348
## iter 490 value 38.654162
## iter 500 value 38.622297
## final value 38.622297
## stopped after 500 iterations
## # weights: 25
## initial value 1373823.380082
## iter 10 value 6253.279474
## iter 20 value 5725.046452
## iter 30 value 5578.631642
## iter 40 value 5492.007544
## iter 50 value 5490.391503
## iter 60 value 5488.023964
## iter 70 value 5465.954053
## iter 80 value 5396.049880
## iter 90 value 5314.708813
## iter 100 value 5304.316394
## iter 110 value 5291.234272
## iter 120 value 5284.433469
## iter 130 value 5108.555670
## iter 140 value 4410.126345
## iter 150 value 3051.899041
## iter 160 value 1695.725783
## iter 170 value 1385.321981
## iter 180 value 1333.434226
## iter 190 value 1322.665567
## iter 200 value 1293.129959
## iter 210 value 1285.278635
## iter 220 value 1280.491969
## iter 230 value 1278.063909
## iter 240 value 1277.371212
## iter 250 value 1277.366433
## final value 1277.366333
## converged
## # weights: 61
## initial value 1364398.873508
## iter 10 value 11905.873694
## iter 20 value 2667.673914
## iter 30 value 2344.956231
## iter 40 value 2182.427176
## iter 50 value 1877.229347
## iter 60 value 1543.205346
## iter 70 value 1329.184053
## iter 80 value 1207.074647
## iter 90 value 1013.733243
## iter 100 value 839.146596
## iter 110 value 801.211787
## iter 120 value 788.557229
## iter 130 value 784.419599
## iter 140 value 782.220552
## iter 150 value 780.065125
## iter 160 value 776.590726
## iter 170 value 775.864267
## iter 180 value 775.730755
## iter 190 value 775.447128
## iter 200 value 775.002045
## iter 210 value 773.817949
## iter 220 value 772.707608
## iter 230 value 770.431204
## iter 240 value 768.420010
## iter 250 value 767.649977
## iter 260 value 767.246021
## iter 270 value 767.157348
## iter 280 value 767.093022
## iter 290 value 767.062501
## iter 300 value 767.059870
## iter 310 value 767.044548
## iter 320 value 766.981909
## iter 330 value 766.816382
## iter 340 value 766.745742
## iter 350 value 766.688524
## iter 360 value 766.675249
## iter 370 value 766.656865
## iter 380 value 766.650118
## iter 390 value 766.642445
## iter 400 value 766.636582
## iter 410 value 766.414065
## iter 420 value 766.353365
## iter 430 value 764.901316
## iter 440 value 760.671367
## iter 450 value 757.236755
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## iter 470 value 754.778566
## iter 480 value 754.637851
## iter 490 value 754.514668
## iter 500 value 754.444447
## final value 754.444447
## stopped after 500 iterations
## # weights: 121
## initial value 1356508.019062
## iter 10 value 1253.270288
## iter 20 value 913.879343
## iter 30 value 736.663029
## iter 40 value 625.605150
## iter 50 value 570.936527
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## iter 90 value 427.615815
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## iter 300 value 273.827177
## iter 310 value 268.627273
## iter 320 value 263.498491
## iter 330 value 258.927127
## iter 340 value 255.650731
## iter 350 value 253.147664
## iter 360 value 250.833061
## iter 370 value 249.681736
## iter 380 value 249.417837
## iter 390 value 249.362799
## iter 400 value 249.292249
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## iter 460 value 248.237593
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## iter 480 value 247.998048
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## iter 500 value 247.987450
## final value 247.987450
## stopped after 500 iterations
## # weights: 181
## initial value 1336740.011918
## iter 10 value 1181.090906
## iter 20 value 759.032193
## iter 30 value 605.281300
## iter 40 value 496.083022
## iter 50 value 439.235673
## iter 60 value 376.746852
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## iter 80 value 286.730812
## iter 90 value 253.279918
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## iter 420 value 84.596774
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## iter 440 value 84.009583
## iter 450 value 83.689817
## iter 460 value 83.261688
## iter 470 value 82.608659
## iter 480 value 81.882700
## iter 490 value 81.511698
## iter 500 value 81.218299
## final value 81.218299
## stopped after 500 iterations
## # weights: 241
## initial value 1399188.347897
## iter 10 value 1409.625308
## iter 20 value 795.392802
## iter 30 value 662.767686
## iter 40 value 547.238388
## iter 50 value 434.876154
## iter 60 value 345.910558
## iter 70 value 298.936100
## iter 80 value 262.613598
## iter 90 value 230.207643
## iter 100 value 206.227783
## iter 110 value 184.471926
## iter 120 value 169.644918
## iter 130 value 161.393077
## iter 140 value 155.801446
## iter 150 value 149.226867
## iter 160 value 139.754978
## iter 170 value 128.640763
## iter 180 value 121.980477
## iter 190 value 117.368666
## iter 200 value 113.337688
## iter 210 value 106.947228
## iter 220 value 101.278063
## iter 230 value 98.297525
## iter 240 value 95.825437
## iter 250 value 93.467519
## iter 260 value 90.223053
## iter 270 value 83.331872
## iter 280 value 78.570581
## iter 290 value 72.948142
## iter 300 value 69.711889
## iter 310 value 64.352941
## iter 320 value 59.498035
## iter 330 value 55.835749
## iter 340 value 53.390554
## iter 350 value 50.640284
## iter 360 value 49.077468
## iter 370 value 47.972969
## iter 380 value 47.192108
## iter 390 value 46.430641
## iter 400 value 45.792278
## iter 410 value 45.137261
## iter 420 value 44.236903
## iter 430 value 43.587327
## iter 440 value 43.209195
## iter 450 value 42.941393
## iter 460 value 42.636025
## iter 470 value 42.363021
## iter 480 value 42.018238
## iter 490 value 41.560420
## iter 500 value 41.417481
## final value 41.417481
## stopped after 500 iterations
## # weights: 25
## initial value 1403786.083865
## iter 10 value 109090.575854
## iter 20 value 12235.275131
## iter 30 value 10458.293873
## iter 40 value 7729.915632
## iter 50 value 7098.728154
## iter 60 value 6936.036218
## iter 70 value 6845.601719
## iter 80 value 6831.785691
## iter 90 value 5306.701518
## iter 100 value 5273.013103
## iter 110 value 5161.193596
## iter 120 value 5115.887293
## iter 130 value 5036.311116
## iter 140 value 4924.146262
## iter 150 value 4875.879097
## iter 160 value 4874.923199
## iter 170 value 4874.905242
## iter 180 value 4874.893180
## iter 190 value 4874.847029
## iter 200 value 4874.809686
## iter 210 value 4874.799255
## iter 220 value 4851.453366
## iter 230 value 4849.099113
## iter 240 value 4846.060725
## iter 250 value 4843.024676
## iter 260 value 4842.887685
## final value 4842.871187
## converged
## # weights: 61
## initial value 1393088.021461
## iter 10 value 3260.795846
## iter 20 value 1384.820038
## iter 30 value 1155.267180
## iter 40 value 994.360390
## iter 50 value 869.922356
## iter 60 value 785.870009
## iter 70 value 731.120828
## iter 80 value 678.912984
## iter 90 value 659.770267
## iter 100 value 649.324934
## iter 110 value 643.098544
## iter 120 value 639.799592
## iter 130 value 639.135584
## iter 140 value 638.384518
## iter 150 value 636.736730
## iter 160 value 633.972176
## iter 170 value 628.206410
## iter 180 value 616.282653
## iter 190 value 595.848528
## iter 200 value 569.711816
## iter 210 value 553.979811
## iter 220 value 545.787447
## iter 230 value 534.945899
## iter 240 value 527.557535
## iter 250 value 525.554159
## iter 260 value 525.277297
## iter 270 value 523.367951
## iter 280 value 521.871232
## iter 290 value 520.658230
## iter 300 value 520.458818
## iter 310 value 518.787633
## iter 320 value 512.649611
## iter 330 value 512.040457
## iter 340 value 511.492133
## iter 350 value 509.990577
## iter 360 value 508.364601
## iter 370 value 508.250028
## iter 380 value 508.247649
## iter 390 value 508.231024
## iter 400 value 508.211727
## iter 410 value 508.064736
## iter 420 value 507.918188
## iter 430 value 507.804248
## iter 440 value 507.720711
## iter 450 value 507.574598
## iter 460 value 507.561893
## iter 470 value 507.561175
## iter 480 value 507.558771
## iter 490 value 507.550793
## final value 507.550416
## converged
## # weights: 121
## initial value 1412465.598450
## iter 10 value 3644.689711
## iter 20 value 1284.277922
## iter 30 value 927.686067
## iter 40 value 858.829070
## iter 50 value 826.437010
## iter 60 value 792.697973
## iter 70 value 734.664321
## iter 80 value 705.302068
## iter 90 value 669.431413
## iter 100 value 615.021180
## iter 110 value 585.162706
## iter 120 value 561.139588
## iter 130 value 531.179863
## iter 140 value 514.026613
## iter 150 value 504.161953
## iter 160 value 490.860952
## iter 170 value 482.631429
## iter 180 value 481.610414
## iter 190 value 479.876922
## iter 200 value 477.322793
## iter 210 value 476.408946
## iter 220 value 476.174850
## iter 230 value 475.395736
## iter 240 value 474.708554
## iter 250 value 473.786295
## iter 260 value 473.523383
## iter 270 value 473.304851
## iter 280 value 472.774154
## iter 290 value 472.398132
## iter 300 value 471.855370
## iter 310 value 470.562349
## iter 320 value 469.715299
## iter 330 value 469.553279
## iter 340 value 469.370841
## iter 350 value 468.942093
## iter 360 value 468.300864
## iter 370 value 468.043216
## iter 380 value 467.725542
## iter 390 value 467.626573
## iter 400 value 467.561666
## iter 410 value 467.513369
## iter 420 value 467.451402
## iter 430 value 467.431944
## iter 440 value 467.405126
## iter 450 value 467.400114
## iter 460 value 467.398315
## final value 467.397823
## converged
## # weights: 181
## initial value 1378212.034278
## iter 10 value 1703.541153
## iter 20 value 1101.212112
## iter 30 value 731.917070
## iter 40 value 568.798338
## iter 50 value 462.899902
## iter 60 value 398.317097
## iter 70 value 360.462422
## iter 80 value 327.909791
## iter 90 value 304.697749
## iter 100 value 282.805433
## iter 110 value 262.578836
## iter 120 value 245.889765
## iter 130 value 229.466097
## iter 140 value 215.270058
## iter 150 value 207.713023
## iter 160 value 200.510845
## iter 170 value 195.583108
## iter 180 value 192.284509
## iter 190 value 188.651419
## iter 200 value 185.605565
## iter 210 value 183.961976
## iter 220 value 182.321087
## iter 230 value 181.391753
## iter 240 value 180.220902
## iter 250 value 178.327857
## iter 260 value 176.647529
## iter 270 value 173.243753
## iter 280 value 170.589587
## iter 290 value 168.088739
## iter 300 value 165.664329
## iter 310 value 164.400616
## iter 320 value 163.630390
## iter 330 value 162.671111
## iter 340 value 161.375750
## iter 350 value 159.708574
## iter 360 value 158.172092
## iter 370 value 157.059211
## iter 380 value 156.773046
## iter 390 value 156.177545
## iter 400 value 154.012173
## iter 410 value 150.051945
## iter 420 value 143.356092
## iter 430 value 139.358932
## iter 440 value 137.001456
## iter 450 value 135.145501
## iter 460 value 133.658801
## iter 470 value 132.715739
## iter 480 value 131.910983
## iter 490 value 131.344263
## iter 500 value 130.791752
## final value 130.791752
## stopped after 500 iterations
## # weights: 241
## initial value 1414928.952290
## iter 10 value 1112.083537
## iter 20 value 820.314474
## iter 30 value 627.938497
## iter 40 value 471.243433
## iter 50 value 352.983889
## iter 60 value 304.959582
## iter 70 value 263.290683
## iter 80 value 200.116239
## iter 90 value 156.462294
## iter 100 value 126.919666
## iter 110 value 108.706463
## iter 120 value 99.617129
## iter 130 value 92.514948
## iter 140 value 86.640786
## iter 150 value 80.254759
## iter 160 value 74.218510
## iter 170 value 68.952178
## iter 180 value 65.317075
## iter 190 value 62.231048
## iter 200 value 59.476403
## iter 210 value 56.405730
## iter 220 value 52.293064
## iter 230 value 47.774677
## iter 240 value 45.708373
## iter 250 value 43.545619
## iter 260 value 41.375606
## iter 270 value 38.989487
## iter 280 value 36.060379
## iter 290 value 34.187959
## iter 300 value 32.880289
## iter 310 value 31.892459
## iter 320 value 31.052977
## iter 330 value 30.432835
## iter 340 value 29.467565
## iter 350 value 28.509170
## iter 360 value 27.523931
## iter 370 value 26.181882
## iter 380 value 25.403279
## iter 390 value 24.759019
## iter 400 value 24.351240
## iter 410 value 24.029185
## iter 420 value 23.818087
## iter 430 value 23.649284
## iter 440 value 23.488856
## iter 450 value 23.266357
## iter 460 value 22.540332
## iter 470 value 22.050322
## iter 480 value 21.749350
## iter 490 value 21.343178
## iter 500 value 21.085269
## final value 21.085269
## stopped after 500 iterations
## # weights: 25
## initial value 1403838.960057
## iter 10 value 506824.749182
## iter 20 value 44386.175785
## iter 30 value 15890.689843
## iter 40 value 9416.351899
## iter 50 value 8490.905770
## iter 60 value 5735.553237
## iter 70 value 5010.229939
## iter 80 value 4047.326041
## iter 90 value 3766.492297
## iter 100 value 2522.448204
## iter 110 value 1765.843199
## iter 120 value 1333.882634
## iter 130 value 1237.414213
## iter 140 value 1229.070896
## iter 150 value 1200.979018
## iter 160 value 1175.562164
## iter 170 value 1171.947238
## iter 180 value 1171.382560
## iter 190 value 1171.379816
## iter 190 value 1171.379813
## iter 200 value 1171.378623
## final value 1171.378169
## converged
## # weights: 61
## initial value 1355729.880341
## iter 10 value 10900.552477
## iter 20 value 6255.930602
## iter 30 value 4978.361437
## iter 40 value 4354.622460
## iter 50 value 3347.467992
## iter 60 value 2270.692950
## iter 70 value 1939.407852
## iter 80 value 1809.152073
## iter 90 value 1627.059129
## iter 100 value 1495.984986
## iter 110 value 1459.819661
## iter 120 value 1435.237228
## iter 130 value 1424.999084
## iter 140 value 1396.703192
## iter 150 value 1245.071424
## iter 160 value 1164.136867
## iter 170 value 1120.667877
## iter 180 value 1077.657534
## iter 190 value 1062.230791
## iter 200 value 1050.255460
## iter 210 value 1024.342159
## iter 220 value 1010.332015
## iter 230 value 988.807795
## iter 240 value 966.806886
## iter 250 value 945.875646
## iter 260 value 941.171965
## iter 270 value 936.601313
## iter 280 value 928.771873
## iter 290 value 924.891796
## iter 300 value 920.281473
## iter 310 value 917.349135
## iter 320 value 917.024758
## iter 330 value 916.934474
## iter 340 value 916.928242
## final value 916.928060
## converged
## # weights: 121
## initial value 1403073.843998
## iter 10 value 2107.523782
## iter 20 value 1362.300897
## iter 30 value 1185.796867
## iter 40 value 1048.747753
## iter 50 value 931.796074
## iter 60 value 855.519058
## iter 70 value 808.389906
## iter 80 value 782.364516
## iter 90 value 767.325278
## iter 100 value 755.388188
## iter 110 value 742.132719
## iter 120 value 726.371402
## iter 130 value 712.753263
## iter 140 value 683.452858
## iter 150 value 665.893149
## iter 160 value 650.454591
## iter 170 value 633.277032
## iter 180 value 612.447425
## iter 190 value 606.462573
## iter 200 value 600.277575
## iter 210 value 595.455302
## iter 220 value 592.182436
## iter 230 value 589.140085
## iter 240 value 586.357115
## iter 250 value 585.349842
## iter 260 value 584.342387
## iter 270 value 581.812184
## iter 280 value 580.365301
## iter 290 value 579.592517
## iter 300 value 579.106209
## iter 310 value 578.146926
## iter 320 value 577.544223
## iter 330 value 576.387130
## iter 340 value 575.632030
## iter 350 value 575.360008
## iter 360 value 575.273223
## iter 370 value 575.252466
## iter 380 value 575.246704
## final value 575.246490
## converged
## # weights: 181
## initial value 1392525.760123
## iter 10 value 1335.243737
## iter 20 value 937.276248
## iter 30 value 741.748519
## iter 40 value 637.818077
## iter 50 value 567.992999
## iter 60 value 517.702860
## iter 70 value 483.021601
## iter 80 value 464.102084
## iter 90 value 446.484026
## iter 100 value 430.887759
## iter 110 value 418.062586
## iter 120 value 408.244518
## iter 130 value 402.325369
## iter 140 value 397.906754
## iter 150 value 394.107682
## iter 160 value 386.600080
## iter 170 value 380.389622
## iter 180 value 375.817339
## iter 190 value 372.784996
## iter 200 value 370.527116
## iter 210 value 368.812003
## iter 220 value 367.364170
## iter 230 value 366.109081
## iter 240 value 362.394829
## iter 250 value 360.921792
## iter 260 value 360.240145
## iter 270 value 359.624404
## iter 280 value 359.210443
## iter 290 value 358.341089
## iter 300 value 355.190865
## iter 310 value 352.535360
## iter 320 value 351.164719
## iter 330 value 349.681241
## iter 340 value 348.278906
## iter 350 value 347.553564
## iter 360 value 346.485252
## iter 370 value 345.802900
## iter 380 value 345.348713
## iter 390 value 344.545281
## iter 400 value 343.145703
## iter 410 value 342.028837
## iter 420 value 340.858727
## iter 430 value 339.902367
## iter 440 value 339.284692
## iter 450 value 338.800417
## iter 460 value 338.495463
## iter 470 value 338.284241
## iter 480 value 338.161969
## iter 490 value 338.108534
## iter 500 value 338.072148
## final value 338.072148
## stopped after 500 iterations
## # weights: 241
## initial value 1389319.093287
## iter 10 value 1355.486815
## iter 20 value 1000.114558
## iter 30 value 805.397807
## iter 40 value 709.435649
## iter 50 value 630.817504
## iter 60 value 577.716356
## iter 70 value 548.936664
## iter 80 value 527.646623
## iter 90 value 501.442998
## iter 100 value 468.056672
## iter 110 value 446.169373
## iter 120 value 430.342575
## iter 130 value 417.665335
## iter 140 value 406.531938
## iter 150 value 397.384910
## iter 160 value 391.721459
## iter 170 value 387.764683
## iter 180 value 383.722419
## iter 190 value 377.288687
## iter 200 value 371.808477
## iter 210 value 367.296776
## iter 220 value 362.981632
## iter 230 value 359.147544
## iter 240 value 354.019349
## iter 250 value 349.746836
## iter 260 value 345.669764
## iter 270 value 341.404619
## iter 280 value 338.552120
## iter 290 value 336.702187
## iter 300 value 335.357523
## iter 310 value 333.603358
## iter 320 value 331.783614
## iter 330 value 329.627795
## iter 340 value 327.266060
## iter 350 value 325.054886
## iter 360 value 323.434266
## iter 370 value 321.904966
## iter 380 value 320.446616
## iter 390 value 318.934533
## iter 400 value 317.335452
## iter 410 value 316.120889
## iter 420 value 314.927469
## iter 430 value 313.609442
## iter 440 value 312.638502
## iter 450 value 311.887785
## iter 460 value 311.300014
## iter 470 value 310.848868
## iter 480 value 310.418241
## iter 490 value 310.237849
## iter 500 value 310.019176
## final value 310.019176
## stopped after 500 iterations
## # weights: 25
## initial value 1416069.583389
## iter 10 value 2238.202111
## iter 20 value 1738.803993
## iter 30 value 1284.351022
## iter 40 value 1131.389143
## iter 50 value 1081.814527
## iter 60 value 1061.382319
## iter 70 value 1048.601237
## iter 80 value 990.430776
## iter 90 value 972.012073
## iter 100 value 967.280400
## iter 110 value 966.332469
## iter 120 value 965.780017
## iter 130 value 959.377795
## iter 140 value 956.241841
## iter 150 value 954.355821
## iter 160 value 954.296917
## iter 170 value 953.246912
## iter 180 value 952.741974
## iter 190 value 952.704013
## iter 200 value 949.971875
## iter 210 value 949.008291
## iter 220 value 948.944734
## iter 230 value 947.015530
## iter 240 value 946.247923
## iter 250 value 945.320073
## iter 260 value 944.904092
## iter 270 value 944.886916
## iter 280 value 944.499011
## iter 290 value 944.033353
## iter 300 value 943.661728
## iter 310 value 943.579053
## iter 320 value 943.577984
## iter 330 value 943.441042
## iter 340 value 943.422131
## iter 350 value 943.418132
## final value 943.417967
## converged
## # weights: 61
## initial value 1365216.099806
## iter 10 value 6421.776890
## iter 20 value 2312.000363
## iter 30 value 1819.398583
## iter 40 value 1502.520580
## iter 50 value 1383.553475
## iter 60 value 1278.944868
## iter 70 value 1207.198119
## iter 80 value 1161.079270
## iter 90 value 1153.772345
## iter 100 value 1114.059747
## iter 110 value 1062.468245
## iter 120 value 1001.220238
## iter 130 value 994.606825
## iter 140 value 992.781361
## iter 150 value 991.429252
## iter 160 value 989.304512
## iter 170 value 987.049758
## iter 180 value 983.614944
## iter 190 value 981.419372
## iter 200 value 976.255426
## iter 210 value 969.667827
## iter 220 value 963.887997
## iter 230 value 938.604985
## iter 240 value 901.043385
## iter 250 value 873.868881
## iter 260 value 822.605610
## iter 270 value 772.383496
## iter 280 value 740.002720
## iter 290 value 735.719149
## iter 300 value 732.670863
## iter 310 value 731.429551
## iter 320 value 730.310025
## iter 330 value 730.305342
## iter 340 value 730.302079
## final value 730.301941
## converged
## # weights: 121
## initial value 1403325.763243
## iter 10 value 1122.487686
## iter 20 value 870.227515
## iter 30 value 727.204114
## iter 40 value 619.104864
## iter 50 value 578.628219
## iter 60 value 521.944199
## iter 70 value 480.942835
## iter 80 value 435.703695
## iter 90 value 415.579434
## iter 100 value 399.878136
## iter 110 value 383.483034
## iter 120 value 366.345877
## iter 130 value 353.867708
## iter 140 value 346.336705
## iter 150 value 340.493317
## iter 160 value 338.023993
## iter 170 value 336.001899
## iter 180 value 328.304142
## iter 190 value 314.658477
## iter 200 value 307.344009
## iter 210 value 303.593217
## iter 220 value 300.228981
## iter 230 value 297.636419
## iter 240 value 294.041175
## iter 250 value 291.364961
## iter 260 value 288.737225
## iter 270 value 286.071399
## iter 280 value 281.079543
## iter 290 value 277.858500
## iter 300 value 273.132394
## iter 310 value 267.807826
## iter 320 value 263.389189
## iter 330 value 261.578534
## iter 340 value 259.530114
## iter 350 value 256.177391
## iter 360 value 254.380968
## iter 370 value 252.597780
## iter 380 value 250.710564
## iter 390 value 249.421687
## iter 400 value 248.534595
## iter 410 value 248.066951
## iter 420 value 247.662992
## iter 430 value 246.979507
## iter 440 value 246.025056
## iter 450 value 245.623730
## iter 460 value 245.473853
## iter 470 value 245.422949
## iter 480 value 245.414909
## iter 490 value 245.408678
## iter 500 value 245.407584
## final value 245.407584
## stopped after 500 iterations
## # weights: 181
## initial value 1361206.670699
## iter 10 value 1295.858029
## iter 20 value 828.104091
## iter 30 value 640.038740
## iter 40 value 509.873449
## iter 50 value 418.032066
## iter 60 value 364.276351
## iter 70 value 305.188981
## iter 80 value 270.269344
## iter 90 value 242.275767
## iter 100 value 226.337498
## iter 110 value 214.348055
## iter 120 value 203.248665
## iter 130 value 196.357158
## iter 140 value 192.667514
## iter 150 value 188.640037
## iter 160 value 185.457041
## iter 170 value 182.851720
## iter 180 value 180.653876
## iter 190 value 177.588265
## iter 200 value 174.258912
## iter 210 value 169.930911
## iter 220 value 167.074952
## iter 230 value 166.433534
## iter 240 value 165.095074
## iter 250 value 163.829953
## iter 260 value 161.979264
## iter 270 value 160.882454
## iter 280 value 160.018837
## iter 290 value 158.952691
## iter 300 value 157.805368
## iter 310 value 156.446891
## iter 320 value 154.915213
## iter 330 value 152.030485
## iter 340 value 149.456178
## iter 350 value 147.074628
## iter 360 value 146.014718
## iter 370 value 145.682585
## iter 380 value 145.483806
## iter 390 value 145.313174
## iter 400 value 145.157224
## iter 410 value 143.826943
## iter 420 value 143.375155
## iter 430 value 143.035746
## iter 440 value 141.406108
## iter 450 value 138.713330
## iter 460 value 136.786968
## iter 470 value 134.950843
## iter 480 value 133.732260
## iter 490 value 132.706544
## iter 500 value 132.037020
## final value 132.037020
## stopped after 500 iterations
## # weights: 241
## initial value 1354588.740780
## iter 10 value 1082.956447
## iter 20 value 749.391734
## iter 30 value 612.049103
## iter 40 value 468.374614
## iter 50 value 360.245307
## iter 60 value 296.264092
## iter 70 value 263.289819
## iter 80 value 218.002100
## iter 90 value 184.676417
## iter 100 value 153.140980
## iter 110 value 125.636393
## iter 120 value 103.308894
## iter 130 value 90.000521
## iter 140 value 82.016647
## iter 150 value 76.519829
## iter 160 value 71.715830
## iter 170 value 66.620242
## iter 180 value 61.051333
## iter 190 value 56.394968
## iter 200 value 53.587763
## iter 210 value 50.891358
## iter 220 value 49.009867
## iter 230 value 47.600313
## iter 240 value 46.454343
## iter 250 value 45.080640
## iter 260 value 43.992991
## iter 270 value 42.643544
## iter 280 value 41.306902
## iter 290 value 40.335477
## iter 300 value 39.757244
## iter 310 value 39.117204
## iter 320 value 38.510405
## iter 330 value 37.651777
## iter 340 value 36.855159
## iter 350 value 36.087352
## iter 360 value 35.567927
## iter 370 value 35.228022
## iter 380 value 34.884248
## iter 390 value 34.511737
## iter 400 value 33.995170
## iter 410 value 33.054014
## iter 420 value 32.118513
## iter 430 value 31.499032
## iter 440 value 31.066530
## iter 450 value 30.775875
## iter 460 value 30.589733
## iter 470 value 30.410489
## iter 480 value 30.152226
## iter 490 value 30.049611
## iter 500 value 29.988686
## final value 29.988686
## stopped after 500 iterations
## # weights: 25
## initial value 1433122.288894
## iter 10 value 9355.237341
## iter 20 value 3640.491481
## iter 30 value 1805.757171
## iter 40 value 1470.570940
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## iter 60 value 1364.137259
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## iter 280 value 1235.007871
## iter 290 value 1234.931889
## iter 300 value 1234.865749
## iter 300 value 1234.865747
## final value 1234.865640
## converged
## # weights: 61
## initial value 1409890.577075
## iter 10 value 57180.463238
## iter 20 value 19955.574565
## iter 30 value 15381.741605
## iter 40 value 9944.453119
## iter 50 value 6119.117484
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## iter 80 value 1164.410276
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## iter 300 value 739.595650
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## iter 470 value 715.440512
## iter 480 value 715.150605
## iter 490 value 714.246981
## iter 500 value 713.665812
## final value 713.665812
## stopped after 500 iterations
## # weights: 121
## initial value 1435570.100722
## iter 10 value 161003.576662
## iter 20 value 9210.730501
## iter 30 value 5644.791893
## iter 40 value 5100.405213
## iter 50 value 4184.659583
## iter 60 value 3372.636716
## iter 70 value 2611.047442
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## iter 480 value 574.158178
## iter 490 value 565.574964
## iter 500 value 543.570045
## final value 543.570045
## stopped after 500 iterations
## # weights: 181
## initial value 1393214.604692
## iter 10 value 1044.378032
## iter 20 value 833.073995
## iter 30 value 693.406663
## iter 40 value 540.787301
## iter 50 value 431.685996
## iter 60 value 364.670579
## iter 70 value 320.152970
## iter 80 value 278.274023
## iter 90 value 248.736476
## iter 100 value 225.284833
## iter 110 value 212.190219
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## iter 130 value 193.461049
## iter 140 value 184.016035
## iter 150 value 173.470433
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## iter 470 value 108.095283
## iter 480 value 107.550481
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## iter 500 value 106.846898
## final value 106.846898
## stopped after 500 iterations
## # weights: 241
## initial value 1375678.698071
## iter 10 value 1633.474674
## iter 20 value 853.425435
## iter 30 value 677.816412
## iter 40 value 555.245483
## iter 50 value 447.420550
## iter 60 value 379.860769
## iter 70 value 337.310393
## iter 80 value 300.906447
## iter 90 value 267.619889
## iter 100 value 230.193146
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## iter 450 value 40.330356
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## iter 470 value 39.173774
## iter 480 value 38.565787
## iter 490 value 38.224971
## iter 500 value 38.128785
## final value 38.128785
## stopped after 500 iterations
## # weights: 25
## initial value 1393796.053730
## iter 10 value 18857.730552
## iter 20 value 9089.766386
## iter 30 value 5218.038485
## iter 40 value 4301.270954
## iter 50 value 3431.792342
## iter 60 value 1713.123297
## iter 70 value 1172.351039
## iter 80 value 1097.952453
## iter 90 value 1091.164129
## iter 100 value 1085.692328
## iter 110 value 1068.756385
## iter 120 value 1062.486142
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## iter 140 value 1059.731593
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## iter 230 value 1025.658111
## iter 240 value 1022.985628
## iter 250 value 1021.218586
## iter 260 value 1020.936926
## final value 1020.936237
## converged
## # weights: 61
## initial value 1415135.101805
## iter 10 value 68135.086655
## iter 20 value 21209.036248
## iter 30 value 13634.985240
## iter 40 value 6768.509531
## iter 50 value 3695.247003
## iter 60 value 1831.902660
## iter 70 value 1320.895551
## iter 80 value 1118.088027
## iter 90 value 946.404895
## iter 100 value 874.382493
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## iter 220 value 726.625235
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## iter 240 value 723.679464
## iter 250 value 723.314921
## iter 260 value 723.285385
## iter 270 value 723.206132
## iter 280 value 723.147279
## iter 290 value 722.880587
## iter 300 value 721.488307
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## iter 320 value 718.863056
## iter 330 value 716.557346
## iter 340 value 715.053585
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## iter 360 value 712.764950
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## iter 460 value 707.418965
## iter 470 value 706.055333
## iter 480 value 704.572061
## iter 490 value 702.579047
## iter 500 value 702.118509
## final value 702.118509
## stopped after 500 iterations
## # weights: 121
## initial value 1400987.024934
## iter 10 value 2135.882190
## iter 20 value 948.926987
## iter 30 value 797.008111
## iter 40 value 689.331918
## iter 50 value 612.213711
## iter 60 value 582.466377
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## iter 470 value 403.212174
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## iter 490 value 401.229760
## iter 500 value 399.788727
## final value 399.788727
## stopped after 500 iterations
## # weights: 181
## initial value 1389588.623932
## iter 10 value 1209.034656
## iter 20 value 800.501744
## iter 30 value 684.614919
## iter 40 value 548.261747
## iter 50 value 432.383495
## iter 60 value 370.828258
## iter 70 value 330.718987
## iter 80 value 276.394771
## iter 90 value 256.048623
## iter 100 value 239.096238
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## iter 470 value 84.148714
## iter 480 value 82.820092
## iter 490 value 81.692135
## iter 500 value 81.063562
## final value 81.063562
## stopped after 500 iterations
## # weights: 241
## initial value 1482965.235766
## iter 10 value 1180.879252
## iter 20 value 814.111251
## iter 30 value 659.409591
## iter 40 value 579.276728
## iter 50 value 463.478803
## iter 60 value 339.511355
## iter 70 value 287.928845
## iter 80 value 247.352125
## iter 90 value 212.411769
## iter 100 value 178.425787
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## iter 120 value 139.148086
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## iter 150 value 105.546412
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## iter 170 value 94.341533
## iter 180 value 89.267502
## iter 190 value 84.656162
## iter 200 value 81.053352
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## iter 280 value 46.182784
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## iter 300 value 39.851089
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## iter 320 value 35.625278
## iter 330 value 33.741665
## iter 340 value 32.275883
## iter 350 value 30.241303
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## iter 370 value 27.588382
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## iter 400 value 24.829598
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## iter 470 value 19.845840
## iter 480 value 19.555142
## iter 490 value 19.415572
## iter 500 value 19.351543
## final value 19.351543
## stopped after 500 iterations
## # weights: 25
## initial value 1385756.953187
## iter 10 value 48172.563992
## iter 20 value 5626.046095
## iter 30 value 4827.184454
## iter 40 value 4684.994733
## iter 50 value 4676.847180
## iter 60 value 4411.624885
## iter 70 value 4337.236652
## iter 80 value 4305.803371
## iter 90 value 4189.023750
## iter 100 value 4111.053620
## iter 110 value 4097.388796
## iter 120 value 4077.868661
## iter 130 value 4058.186724
## iter 140 value 4024.363131
## iter 150 value 4001.599711
## iter 160 value 3992.031933
## iter 170 value 3985.928854
## iter 180 value 3844.621387
## iter 190 value 3731.034289
## iter 200 value 3079.219451
## iter 210 value 2651.403268
## iter 220 value 1869.076982
## iter 230 value 1447.981206
## iter 240 value 1348.279574
## iter 250 value 1315.701274
## iter 260 value 1311.012222
## iter 270 value 1296.442451
## iter 280 value 1286.097193
## iter 290 value 1282.492933
## iter 300 value 1280.901038
## iter 310 value 1280.461069
## iter 320 value 1280.174577
## iter 330 value 1278.815732
## iter 340 value 1277.895013
## iter 350 value 1277.342544
## iter 360 value 1277.152740
## final value 1277.152420
## converged
## # weights: 61
## initial value 1399122.577923
## iter 10 value 8492.495248
## iter 20 value 3737.440331
## iter 30 value 3164.521992
## iter 40 value 2751.922036
## iter 50 value 2329.764530
## iter 60 value 1958.142304
## iter 70 value 1797.442093
## iter 80 value 1754.922723
## iter 90 value 1736.232238
## iter 100 value 1695.533168
## iter 110 value 1665.867370
## iter 120 value 1661.013312
## iter 130 value 1658.112296
## iter 140 value 1625.844994
## iter 150 value 1467.383800
## iter 160 value 1287.902896
## iter 170 value 1170.546110
## iter 180 value 1132.947532
## iter 190 value 1105.897044
## iter 200 value 1067.354388
## iter 210 value 1050.529468
## iter 220 value 1040.752608
## iter 230 value 1033.392759
## iter 240 value 1025.014724
## iter 250 value 1020.459613
## iter 260 value 1019.574775
## iter 270 value 1013.798544
## iter 280 value 1011.793458
## iter 290 value 1011.217186
## iter 300 value 1009.965131
## iter 310 value 1008.592208
## iter 320 value 1007.325138
## iter 330 value 1005.693383
## iter 340 value 1005.025584
## iter 350 value 1004.531955
## iter 360 value 1004.318232
## iter 370 value 1003.417727
## iter 380 value 1002.715534
## iter 390 value 1002.463412
## iter 400 value 1002.380161
## final value 1002.380055
## converged
## # weights: 121
## initial value 1413298.565646
## iter 10 value 1430.562028
## iter 20 value 831.031857
## iter 30 value 658.690126
## iter 40 value 549.857548
## iter 50 value 492.301158
## iter 60 value 457.480271
## iter 70 value 433.811995
## iter 80 value 416.642943
## iter 90 value 398.975130
## iter 100 value 381.526442
## iter 110 value 361.337671
## iter 120 value 340.486234
## iter 130 value 330.183381
## iter 140 value 317.941881
## iter 150 value 309.763997
## iter 160 value 301.814000
## iter 170 value 296.264440
## iter 180 value 292.844882
## iter 190 value 288.592533
## iter 200 value 284.673743
## iter 210 value 278.572477
## iter 220 value 273.092553
## iter 230 value 265.569153
## iter 240 value 259.581992
## iter 250 value 257.438313
## iter 260 value 256.415332
## iter 270 value 253.732371
## iter 280 value 250.500840
## iter 290 value 247.289329
## iter 300 value 244.434309
## iter 310 value 241.818178
## iter 320 value 239.240053
## iter 330 value 236.289890
## iter 340 value 230.583178
## iter 350 value 224.751983
## iter 360 value 222.301082
## iter 370 value 220.534332
## iter 380 value 219.531307
## iter 390 value 219.156432
## iter 400 value 218.913938
## iter 410 value 218.709599
## iter 420 value 218.642769
## iter 430 value 218.467114
## iter 440 value 218.375785
## iter 450 value 218.158281
## iter 460 value 218.078756
## iter 470 value 217.957158
## iter 480 value 217.891003
## iter 490 value 217.872053
## iter 500 value 217.871202
## final value 217.871202
## stopped after 500 iterations
## # weights: 181
## initial value 1411394.011152
## iter 10 value 1138.201808
## iter 20 value 771.813077
## iter 30 value 609.921321
## iter 40 value 473.322909
## iter 50 value 382.024387
## iter 60 value 338.760105
## iter 70 value 295.291145
## iter 80 value 251.255562
## iter 90 value 214.970369
## iter 100 value 199.301783
## iter 110 value 188.836892
## iter 120 value 177.175882
## iter 130 value 167.993402
## iter 140 value 157.269320
## iter 150 value 143.186740
## iter 160 value 129.694431
## iter 170 value 121.078541
## iter 180 value 111.482796
## iter 190 value 102.003307
## iter 200 value 96.673977
## iter 210 value 91.251334
## iter 220 value 85.943837
## iter 230 value 82.719689
## iter 240 value 79.065838
## iter 250 value 76.309919
## iter 260 value 74.108705
## iter 270 value 73.123644
## iter 280 value 72.172961
## iter 290 value 71.759515
## iter 300 value 71.367803
## iter 310 value 70.968709
## iter 320 value 70.501713
## iter 330 value 70.255363
## iter 340 value 70.122502
## iter 350 value 69.962639
## iter 360 value 69.874514
## iter 370 value 69.825143
## iter 380 value 69.794042
## iter 390 value 69.718004
## iter 400 value 69.618456
## iter 410 value 69.513349
## iter 420 value 69.303430
## iter 430 value 69.182564
## iter 440 value 69.093405
## iter 450 value 68.982104
## iter 460 value 68.868635
## iter 470 value 68.715874
## iter 480 value 68.271789
## iter 490 value 66.847164
## iter 500 value 65.995674
## final value 65.995674
## stopped after 500 iterations
## # weights: 241
## initial value 1442424.607015
## iter 10 value 1308.114413
## iter 20 value 739.043313
## iter 30 value 607.718410
## iter 40 value 519.468246
## iter 50 value 433.357689
## iter 60 value 362.754740
## iter 70 value 324.148434
## iter 80 value 295.712863
## iter 90 value 263.848253
## iter 100 value 240.841996
## iter 110 value 212.429790
## iter 120 value 185.662652
## iter 130 value 164.931948
## iter 140 value 141.721610
## iter 150 value 126.710512
## iter 160 value 117.472936
## iter 170 value 108.069620
## iter 180 value 101.634520
## iter 190 value 90.423121
## iter 200 value 81.015868
## iter 210 value 73.964015
## iter 220 value 67.833524
## iter 230 value 64.017169
## iter 240 value 60.042701
## iter 250 value 56.459999
## iter 260 value 54.295065
## iter 270 value 52.049504
## iter 280 value 49.624346
## iter 290 value 48.404838
## iter 300 value 46.712110
## iter 310 value 44.551690
## iter 320 value 43.024047
## iter 330 value 41.128716
## iter 340 value 39.114657
## iter 350 value 36.356922
## iter 360 value 34.593786
## iter 370 value 33.541986
## iter 380 value 32.857202
## iter 390 value 32.321888
## iter 400 value 31.713845
## iter 410 value 31.328039
## iter 420 value 30.922180
## iter 430 value 30.410621
## iter 440 value 29.946491
## iter 450 value 29.559267
## iter 460 value 29.084275
## iter 470 value 28.642889
## iter 480 value 28.374166
## iter 490 value 28.220594
## iter 500 value 28.154890
## final value 28.154890
## stopped after 500 iterations
## # weights: 25
## initial value 1410405.784373
## iter 10 value 17593.289730
## iter 20 value 15479.109890
## iter 30 value 14370.467707
## iter 40 value 13529.388845
## iter 50 value 8311.475210
## iter 60 value 3074.308761
## iter 70 value 1828.148546
## iter 80 value 1447.956967
## iter 90 value 1387.715565
## iter 100 value 1338.917764
## iter 110 value 1283.525839
## iter 120 value 1279.470971
## iter 130 value 1279.434373
## iter 130 value 1279.434362
## final value 1279.434362
## converged
## # weights: 61
## initial value 1390495.319656
## iter 10 value 4262.310965
## iter 20 value 3135.835201
## iter 30 value 2607.914753
## iter 40 value 2294.598266
## iter 50 value 1947.171829
## iter 60 value 1749.449614
## iter 70 value 1682.371193
## iter 80 value 1570.643663
## iter 90 value 1369.438522
## iter 100 value 1238.022184
## iter 110 value 1114.010623
## iter 120 value 1048.198666
## iter 130 value 1024.750346
## iter 140 value 1007.624594
## iter 150 value 965.220398
## iter 160 value 904.860688
## iter 170 value 876.086587
## iter 180 value 851.829014
## iter 190 value 819.365860
## iter 200 value 786.670386
## iter 210 value 774.455769
## iter 220 value 764.041146
## iter 230 value 755.453109
## iter 240 value 751.936619
## iter 250 value 749.143105
## iter 260 value 748.600057
## iter 270 value 747.053135
## iter 280 value 742.237694
## iter 290 value 736.071864
## iter 300 value 732.123105
## iter 310 value 731.152801
## iter 320 value 730.546548
## iter 330 value 730.496267
## iter 340 value 730.494612
## final value 730.494297
## converged
## # weights: 121
## initial value 1428212.309370
## iter 10 value 3686.633408
## iter 20 value 1838.579675
## iter 30 value 1330.776320
## iter 40 value 1189.277562
## iter 50 value 1096.744237
## iter 60 value 1055.879668
## iter 70 value 1014.891592
## iter 80 value 976.702369
## iter 90 value 941.322410
## iter 100 value 866.271912
## iter 110 value 817.432624
## iter 120 value 784.615994
## iter 130 value 754.499144
## iter 140 value 728.037059
## iter 150 value 711.028241
## iter 160 value 696.247781
## iter 170 value 681.102139
## iter 180 value 668.257284
## iter 190 value 660.414762
## iter 200 value 641.482853
## iter 210 value 626.642362
## iter 220 value 613.256818
## iter 230 value 606.607440
## iter 240 value 597.809778
## iter 250 value 595.791958
## iter 260 value 594.046556
## iter 270 value 592.251462
## iter 280 value 590.834968
## iter 290 value 589.602787
## iter 300 value 588.554477
## iter 310 value 585.820644
## iter 320 value 582.637223
## iter 330 value 580.338698
## iter 340 value 578.697859
## iter 350 value 575.668634
## iter 360 value 571.347476
## iter 370 value 568.293413
## iter 380 value 564.898680
## iter 390 value 564.071284
## iter 400 value 563.636708
## iter 410 value 563.228502
## iter 420 value 563.144202
## iter 430 value 563.128731
## iter 440 value 563.125472
## iter 450 value 563.124620
## final value 563.124485
## converged
## # weights: 181
## initial value 1345794.900611
## iter 10 value 1269.060245
## iter 20 value 861.238077
## iter 30 value 708.115266
## iter 40 value 632.558192
## iter 50 value 566.335817
## iter 60 value 530.295342
## iter 70 value 494.268841
## iter 80 value 475.722849
## iter 90 value 461.686500
## iter 100 value 449.808581
## iter 110 value 438.999394
## iter 120 value 432.328629
## iter 130 value 427.636549
## iter 140 value 424.115737
## iter 150 value 419.410500
## iter 160 value 415.638753
## iter 170 value 412.910816
## iter 180 value 410.519073
## iter 190 value 407.769599
## iter 200 value 404.804591
## iter 210 value 401.424497
## iter 220 value 400.311839
## iter 230 value 398.983355
## iter 240 value 397.742900
## iter 250 value 396.350654
## iter 260 value 395.551638
## iter 270 value 394.917512
## iter 280 value 393.951534
## iter 290 value 393.186533
## iter 300 value 392.509012
## iter 310 value 392.024675
## iter 320 value 391.793276
## iter 330 value 391.497790
## iter 340 value 390.992714
## iter 350 value 389.928879
## iter 360 value 388.656630
## iter 370 value 387.838451
## iter 380 value 387.167161
## iter 390 value 386.024954
## iter 400 value 384.929248
## iter 410 value 384.282733
## iter 420 value 383.735951
## iter 430 value 383.369506
## iter 440 value 383.217827
## iter 450 value 383.149137
## iter 460 value 383.063970
## iter 470 value 382.964769
## iter 480 value 382.745338
## iter 490 value 381.873027
## iter 500 value 380.320393
## final value 380.320393
## stopped after 500 iterations
## # weights: 241
## initial value 1442893.265919
## iter 10 value 1865.163565
## iter 20 value 953.755893
## iter 30 value 709.643692
## iter 40 value 602.663354
## iter 50 value 546.960928
## iter 60 value 511.975300
## iter 70 value 488.632974
## iter 80 value 476.636292
## iter 90 value 467.428162
## iter 100 value 460.469860
## iter 110 value 455.010734
## iter 120 value 450.916494
## iter 130 value 444.209310
## iter 140 value 437.537908
## iter 150 value 431.553150
## iter 160 value 425.673790
## iter 170 value 418.708580
## iter 180 value 412.090185
## iter 190 value 403.777857
## iter 200 value 398.676912
## iter 210 value 393.541194
## iter 220 value 389.667371
## iter 230 value 385.144506
## iter 240 value 381.277392
## iter 250 value 376.866504
## iter 260 value 372.120153
## iter 270 value 366.715133
## iter 280 value 360.492842
## iter 290 value 355.775235
## iter 300 value 352.415857
## iter 310 value 349.824220
## iter 320 value 346.198988
## iter 330 value 343.231419
## iter 340 value 340.795564
## iter 350 value 338.678983
## iter 360 value 336.201743
## iter 370 value 334.083586
## iter 380 value 331.356664
## iter 390 value 328.056124
## iter 400 value 325.803056
## iter 410 value 324.324926
## iter 420 value 322.705850
## iter 430 value 321.239330
## iter 440 value 320.085876
## iter 450 value 319.363352
## iter 460 value 318.809280
## iter 470 value 318.312681
## iter 480 value 317.728673
## iter 490 value 317.400780
## iter 500 value 316.925133
## final value 316.925133
## stopped after 500 iterations
## # weights: 25
## initial value 1376505.554393
## iter 10 value 13868.910719
## iter 20 value 8980.535514
## iter 30 value 6038.288622
## iter 40 value 5217.029749
## iter 50 value 5179.685831
## iter 60 value 5080.343651
## iter 70 value 2615.825485
## iter 80 value 1623.733020
## iter 90 value 1383.242726
## iter 100 value 1321.905558
## iter 110 value 1307.565600
## iter 120 value 1305.432616
## iter 130 value 1299.419336
## iter 140 value 1284.128492
## iter 150 value 1249.348190
## iter 160 value 1124.950146
## iter 170 value 1099.944447
## iter 180 value 1090.267213
## iter 190 value 1090.168701
## iter 200 value 1090.153358
## iter 210 value 1089.992062
## final value 1089.809081
## converged
## # weights: 61
## initial value 1408902.183080
## iter 10 value 12056.134414
## iter 20 value 6786.434834
## iter 30 value 4103.598224
## iter 40 value 2740.811109
## iter 50 value 2206.431614
## iter 60 value 1985.524291
## iter 70 value 1895.275355
## iter 80 value 1873.187740
## iter 90 value 1854.557052
## iter 100 value 1689.612057
## iter 110 value 1637.186818
## iter 120 value 1582.134762
## iter 130 value 1572.210134
## iter 140 value 1568.897402
## iter 150 value 1542.925975
## iter 160 value 1502.448869
## iter 170 value 1445.021854
## iter 180 value 1399.038459
## iter 190 value 1374.345424
## iter 200 value 1363.518485
## iter 210 value 1338.337040
## iter 220 value 1290.280435
## iter 230 value 1216.734581
## iter 240 value 1153.426899
## iter 250 value 1007.168582
## iter 260 value 892.692768
## iter 270 value 866.526303
## iter 280 value 859.414305
## iter 290 value 851.906647
## iter 300 value 849.736325
## iter 310 value 846.871904
## iter 320 value 837.461969
## iter 330 value 817.559288
## iter 340 value 788.826860
## iter 350 value 769.561769
## iter 360 value 758.767509
## iter 370 value 752.466283
## iter 380 value 739.150897
## iter 390 value 732.598750
## iter 400 value 723.794736
## iter 410 value 717.847091
## iter 420 value 705.051669
## iter 430 value 696.083845
## iter 440 value 685.644900
## iter 450 value 680.108713
## iter 460 value 676.972635
## iter 470 value 674.410857
## iter 480 value 673.415600
## iter 490 value 660.386460
## iter 500 value 645.396803
## final value 645.396803
## stopped after 500 iterations
## # weights: 121
## initial value 1411255.962266
## iter 10 value 1490.381268
## iter 20 value 905.060486
## iter 30 value 683.587451
## iter 40 value 598.317435
## iter 50 value 534.422245
## iter 60 value 441.273190
## iter 70 value 414.410749
## iter 80 value 390.902814
## iter 90 value 362.980172
## iter 100 value 342.012342
## iter 110 value 326.031875
## iter 120 value 312.783921
## iter 130 value 300.929641
## iter 140 value 291.513548
## iter 150 value 284.126638
## iter 160 value 280.585951
## iter 170 value 276.933450
## iter 180 value 274.348873
## iter 190 value 272.347741
## iter 200 value 269.519700
## iter 210 value 264.517179
## iter 220 value 259.249750
## iter 230 value 255.828173
## iter 240 value 253.653170
## iter 250 value 252.758477
## iter 260 value 252.453785
## iter 270 value 251.602907
## iter 280 value 250.131769
## iter 290 value 248.704165
## iter 300 value 246.564365
## iter 310 value 245.304647
## iter 320 value 244.549361
## iter 330 value 243.003802
## iter 340 value 241.492672
## iter 350 value 240.785929
## iter 360 value 240.328112
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## iter 380 value 239.681435
## iter 390 value 239.559955
## iter 400 value 239.442395
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## iter 420 value 239.211569
## iter 430 value 239.118821
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## iter 450 value 238.840068
## iter 460 value 238.780848
## iter 470 value 238.753874
## iter 480 value 238.743771
## iter 490 value 238.738167
## iter 500 value 238.736915
## final value 238.736915
## stopped after 500 iterations
## # weights: 181
## initial value 1434844.665530
## iter 10 value 1185.311557
## iter 20 value 729.008955
## iter 30 value 585.632432
## iter 40 value 492.309969
## iter 50 value 380.691003
## iter 60 value 317.470154
## iter 70 value 291.389197
## iter 80 value 264.042453
## iter 90 value 238.102406
## iter 100 value 219.855123
## iter 110 value 204.286285
## iter 120 value 193.042675
## iter 130 value 185.969703
## iter 140 value 176.668448
## iter 150 value 166.170960
## iter 160 value 157.944158
## iter 170 value 152.446573
## iter 180 value 147.904029
## iter 190 value 142.566184
## iter 200 value 137.594204
## iter 210 value 134.135272
## iter 220 value 131.982816
## iter 230 value 130.113828
## iter 240 value 125.477843
## iter 250 value 122.079830
## iter 260 value 120.025121
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## iter 280 value 117.421662
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## iter 330 value 113.224084
## iter 340 value 112.765528
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## iter 390 value 110.713179
## iter 400 value 110.219483
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## iter 460 value 105.617135
## iter 470 value 104.993454
## iter 480 value 104.626200
## iter 490 value 104.389374
## iter 500 value 104.196012
## final value 104.196012
## stopped after 500 iterations
## # weights: 241
## initial value 1382697.281020
## iter 10 value 1494.648186
## iter 20 value 712.308506
## iter 30 value 527.943661
## iter 40 value 409.807358
## iter 50 value 332.540763
## iter 60 value 274.215678
## iter 70 value 219.888255
## iter 80 value 193.585897
## iter 90 value 171.242988
## iter 100 value 155.527566
## iter 110 value 143.697548
## iter 120 value 134.031114
## iter 130 value 124.451259
## iter 140 value 113.994376
## iter 150 value 106.927591
## iter 160 value 102.613632
## iter 170 value 99.022618
## iter 180 value 93.738847
## iter 190 value 89.252408
## iter 200 value 85.705674
## iter 210 value 82.950109
## iter 220 value 79.198687
## iter 230 value 75.781224
## iter 240 value 72.628231
## iter 250 value 70.803989
## iter 260 value 69.114068
## iter 270 value 67.993148
## iter 280 value 67.196932
## iter 290 value 66.207836
## iter 300 value 65.201641
## iter 310 value 64.221914
## iter 320 value 63.486784
## iter 330 value 62.564274
## iter 340 value 61.554905
## iter 350 value 60.544679
## iter 360 value 59.622188
## iter 370 value 59.016570
## iter 380 value 58.374077
## iter 390 value 57.713778
## iter 400 value 57.231774
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## iter 420 value 56.448915
## iter 430 value 56.160388
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## iter 450 value 55.700594
## iter 460 value 55.378283
## iter 470 value 55.087046
## iter 480 value 54.762523
## iter 490 value 54.600801
## iter 500 value 54.550072
## final value 54.550072
## stopped after 500 iterations
## # weights: 25
## initial value 1409918.064319
## iter 10 value 16893.263944
## iter 20 value 15302.774225
## iter 30 value 12099.583290
## iter 40 value 9192.735113
## iter 50 value 8888.488887
## iter 60 value 8839.652696
## iter 70 value 6718.960099
## iter 80 value 5337.688832
## iter 90 value 4834.711055
## iter 100 value 3352.467220
## iter 110 value 2894.156822
## iter 120 value 2316.224239
## iter 130 value 2261.674947
## iter 140 value 2261.422176
## iter 150 value 2259.329692
## iter 160 value 1357.506671
## iter 170 value 1180.449218
## iter 180 value 974.026953
## iter 190 value 874.314257
## iter 200 value 859.967624
## iter 210 value 857.026931
## iter 220 value 855.064715
## iter 230 value 853.108099
## iter 240 value 845.368977
## iter 250 value 837.708381
## iter 260 value 833.643959
## iter 270 value 833.214984
## iter 280 value 831.791996
## iter 290 value 830.341479
## iter 300 value 830.215437
## iter 310 value 830.196424
## final value 830.196334
## converged
## # weights: 61
## initial value 1408705.009561
## iter 10 value 42011.707047
## iter 20 value 18061.828053
## iter 30 value 12755.860611
## iter 40 value 7874.913856
## iter 50 value 5827.241719
## iter 60 value 2231.529316
## iter 70 value 1312.683623
## iter 80 value 1156.240746
## iter 90 value 1115.120369
## iter 100 value 1096.877136
## iter 110 value 1091.418821
## iter 120 value 1083.951402
## iter 130 value 1083.099785
## iter 140 value 1051.439555
## iter 150 value 958.466483
## iter 160 value 923.329956
## iter 170 value 910.887116
## iter 180 value 898.805779
## iter 190 value 892.868977
## iter 200 value 892.679045
## iter 210 value 891.108921
## iter 220 value 889.135654
## iter 230 value 871.444180
## iter 240 value 870.641115
## iter 250 value 847.287042
## iter 260 value 782.500123
## iter 270 value 772.867155
## iter 280 value 772.136430
## iter 290 value 771.106383
## iter 300 value 738.407268
## iter 310 value 715.016983
## iter 320 value 700.098522
## iter 330 value 688.514464
## iter 340 value 676.040525
## iter 350 value 673.901242
## iter 360 value 673.815076
## iter 370 value 672.634777
## iter 380 value 671.376641
## iter 390 value 670.264855
## iter 400 value 668.887482
## iter 410 value 667.570217
## iter 420 value 665.456823
## iter 430 value 661.762575
## iter 440 value 661.576117
## iter 450 value 661.077477
## iter 460 value 660.645482
## iter 470 value 660.124962
## iter 480 value 660.115285
## iter 490 value 659.814365
## iter 500 value 658.537665
## final value 658.537665
## stopped after 500 iterations
## # weights: 121
## initial value 1472655.813606
## iter 10 value 1955.678658
## iter 20 value 967.138206
## iter 30 value 740.212796
## iter 40 value 608.146968
## iter 50 value 540.375532
## iter 60 value 498.843832
## iter 70 value 453.342288
## iter 80 value 395.636875
## iter 90 value 369.976050
## iter 100 value 342.689526
## iter 110 value 325.108109
## iter 120 value 306.182809
## iter 130 value 300.342116
## iter 140 value 294.273172
## iter 150 value 284.756573
## iter 160 value 279.982611
## iter 170 value 275.678463
## iter 180 value 272.371894
## iter 190 value 269.176347
## iter 200 value 265.398931
## iter 210 value 263.277047
## iter 220 value 261.840700
## iter 230 value 259.410334
## iter 240 value 257.320412
## iter 250 value 256.164856
## iter 260 value 255.704230
## iter 270 value 254.796339
## iter 280 value 253.489994
## iter 290 value 252.506774
## iter 300 value 251.063705
## iter 310 value 249.139549
## iter 320 value 245.508092
## iter 330 value 241.963948
## iter 340 value 239.468051
## iter 350 value 238.006131
## iter 360 value 237.436581
## iter 370 value 237.208327
## iter 380 value 236.880516
## iter 390 value 236.495119
## iter 400 value 235.407576
## iter 410 value 233.645776
## iter 420 value 231.394869
## iter 430 value 228.652856
## iter 440 value 227.900540
## iter 450 value 227.482930
## iter 460 value 226.739046
## iter 470 value 225.402077
## iter 480 value 223.340278
## iter 490 value 222.201775
## iter 500 value 222.107644
## final value 222.107644
## stopped after 500 iterations
## # weights: 181
## initial value 1383724.921133
## iter 10 value 1140.656765
## iter 20 value 686.973616
## iter 30 value 559.317301
## iter 40 value 468.093637
## iter 50 value 389.913350
## iter 60 value 343.378496
## iter 70 value 318.371112
## iter 80 value 294.147930
## iter 90 value 263.745853
## iter 100 value 241.179235
## iter 110 value 228.120881
## iter 120 value 213.568472
## iter 130 value 200.768174
## iter 140 value 185.377903
## iter 150 value 174.573550
## iter 160 value 163.500204
## iter 170 value 151.740527
## iter 180 value 139.544220
## iter 190 value 132.752271
## iter 200 value 125.941150
## iter 210 value 120.002828
## iter 220 value 114.665134
## iter 230 value 109.860045
## iter 240 value 107.289971
## iter 250 value 104.186802
## iter 260 value 99.617444
## iter 270 value 96.797307
## iter 280 value 94.459654
## iter 290 value 93.375418
## iter 300 value 92.080426
## iter 310 value 90.662937
## iter 320 value 89.807696
## iter 330 value 88.390869
## iter 340 value 86.613391
## iter 350 value 85.159823
## iter 360 value 84.438102
## iter 370 value 84.154286
## iter 380 value 83.962719
## iter 390 value 83.727025
## iter 400 value 83.339513
## iter 410 value 83.007732
## iter 420 value 82.647262
## iter 430 value 82.240526
## iter 440 value 81.336969
## iter 450 value 80.335584
## iter 460 value 79.727292
## iter 470 value 79.484365
## iter 480 value 79.144365
## iter 490 value 78.480179
## iter 500 value 78.094737
## final value 78.094737
## stopped after 500 iterations
## # weights: 241
## initial value 1394784.426832
## iter 10 value 1253.851802
## iter 20 value 742.479039
## iter 30 value 578.272411
## iter 40 value 489.540601
## iter 50 value 398.356679
## iter 60 value 338.733747
## iter 70 value 303.363370
## iter 80 value 276.360479
## iter 90 value 243.114193
## iter 100 value 220.207056
## iter 110 value 197.899952
## iter 120 value 185.889807
## iter 130 value 173.453186
## iter 140 value 160.775360
## iter 150 value 151.121251
## iter 160 value 142.816426
## iter 170 value 136.781967
## iter 180 value 130.793148
## iter 190 value 124.314552
## iter 200 value 117.383339
## iter 210 value 110.886471
## iter 220 value 98.361320
## iter 230 value 85.323995
## iter 240 value 73.933832
## iter 250 value 66.368620
## iter 260 value 60.642077
## iter 270 value 57.336438
## iter 280 value 55.126354
## iter 290 value 53.380265
## iter 300 value 51.583153
## iter 310 value 49.639812
## iter 320 value 47.983363
## iter 330 value 46.563244
## iter 340 value 45.087715
## iter 350 value 43.629601
## iter 360 value 42.683533
## iter 370 value 42.081269
## iter 380 value 41.567769
## iter 390 value 40.999653
## iter 400 value 40.584576
## iter 410 value 40.123338
## iter 420 value 39.749706
## iter 430 value 39.406155
## iter 440 value 38.870857
## iter 450 value 38.439063
## iter 460 value 38.013985
## iter 470 value 37.611351
## iter 480 value 37.257887
## iter 490 value 37.106855
## iter 500 value 37.072628
## final value 37.072628
## stopped after 500 iterations
## # weights: 25
## initial value 1407644.724686
## iter 10 value 16277.244852
## iter 20 value 15396.841019
## iter 30 value 14813.892143
## iter 40 value 9830.005938
## iter 50 value 7195.441831
## iter 60 value 6019.399218
## iter 70 value 1952.695987
## iter 80 value 1328.307151
## iter 90 value 1309.660242
## iter 100 value 1296.229323
## iter 110 value 1285.421011
## iter 120 value 1279.037974
## iter 130 value 1242.900639
## iter 140 value 1233.141077
## iter 150 value 1185.378860
## iter 160 value 1158.208431
## iter 170 value 1149.625412
## iter 180 value 1148.725795
## iter 190 value 1135.448805
## iter 200 value 1120.301644
## iter 210 value 1119.240143
## iter 220 value 1118.506630
## iter 230 value 1118.422777
## iter 240 value 1118.411343
## iter 250 value 1117.964757
## iter 260 value 1117.745636
## iter 270 value 1117.344654
## iter 280 value 1117.277587
## iter 290 value 1117.272064
## iter 300 value 1117.196742
## iter 310 value 1117.111445
## iter 320 value 1117.044745
## iter 330 value 1117.017393
## iter 330 value 1117.017386
## iter 330 value 1117.017377
## final value 1117.017377
## converged
## # weights: 61
## initial value 1397988.989015
## iter 10 value 3669.274819
## iter 20 value 2112.660533
## iter 30 value 1640.147688
## iter 40 value 1191.984673
## iter 50 value 961.632449
## iter 60 value 859.877114
## iter 70 value 809.550392
## iter 80 value 783.265856
## iter 90 value 771.364396
## iter 100 value 763.211778
## iter 110 value 749.820589
## iter 120 value 741.040302
## iter 130 value 710.571399
## iter 140 value 667.403487
## iter 150 value 626.883736
## iter 160 value 593.385051
## iter 170 value 582.309648
## iter 180 value 576.042018
## iter 190 value 569.169888
## iter 200 value 561.762029
## iter 210 value 556.103482
## iter 220 value 548.415678
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## iter 480 value 528.252433
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## iter 500 value 528.250943
## final value 528.250943
## stopped after 500 iterations
## # weights: 121
## initial value 1358735.038285
## iter 10 value 2463.261607
## iter 20 value 1216.252666
## iter 30 value 897.187740
## iter 40 value 696.325529
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## iter 500 value 346.213570
## final value 346.213570
## stopped after 500 iterations
## # weights: 181
## initial value 1370357.622741
## iter 10 value 1121.493856
## iter 20 value 695.219403
## iter 30 value 581.608939
## iter 40 value 492.097311
## iter 50 value 448.804968
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## iter 480 value 81.410076
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## iter 500 value 78.791758
## final value 78.791758
## stopped after 500 iterations
## # weights: 241
## initial value 1394531.629793
## iter 10 value 1556.418468
## iter 20 value 728.020973
## iter 30 value 569.930840
## iter 40 value 455.452673
## iter 50 value 361.510200
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## iter 70 value 248.520215
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## iter 90 value 168.119152
## iter 100 value 149.200400
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## iter 150 value 92.102976
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## iter 170 value 83.242311
## iter 180 value 79.704211
## iter 190 value 75.545963
## iter 200 value 70.224061
## iter 210 value 64.986427
## iter 220 value 61.292671
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## iter 240 value 54.032832
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## iter 310 value 38.293568
## iter 320 value 37.293692
## iter 330 value 36.415948
## iter 340 value 35.432999
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## iter 360 value 33.672526
## iter 370 value 32.994990
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## iter 390 value 31.583076
## iter 400 value 31.181869
## iter 410 value 30.753296
## iter 420 value 30.464756
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## iter 470 value 28.610054
## iter 480 value 28.406269
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## iter 500 value 28.274641
## final value 28.274641
## stopped after 500 iterations
## # weights: 25
## initial value 1393612.292566
## iter 10 value 6291.180642
## iter 20 value 6265.542738
## iter 20 value 6265.542688
## iter 20 value 6265.542688
## final value 6265.542688
## converged
## # weights: 61
## initial value 1440695.729584
## iter 10 value 62290.459830
## iter 20 value 4751.281675
## iter 30 value 3775.951060
## iter 40 value 3030.171394
## iter 50 value 1510.024531
## iter 60 value 1357.852890
## iter 70 value 1259.185632
## iter 80 value 1073.131229
## iter 90 value 988.031675
## iter 100 value 953.456285
## iter 110 value 934.835086
## iter 120 value 917.985774
## iter 130 value 894.530240
## iter 140 value 873.971712
## iter 150 value 839.706330
## iter 160 value 789.367274
## iter 170 value 765.278573
## iter 180 value 755.006050
## iter 190 value 750.621252
## iter 200 value 745.743410
## iter 210 value 736.405121
## iter 220 value 724.966776
## iter 230 value 716.342378
## iter 240 value 713.736118
## iter 250 value 711.474880
## iter 260 value 708.880750
## iter 270 value 704.791343
## iter 280 value 700.433575
## iter 290 value 697.728291
## iter 300 value 695.677807
## iter 310 value 695.228944
## iter 320 value 694.280494
## iter 330 value 693.561119
## iter 340 value 691.330622
## iter 350 value 688.668165
## iter 360 value 686.852851
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## iter 380 value 682.499758
## iter 390 value 678.883741
## iter 400 value 666.755233
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## iter 420 value 634.509208
## iter 430 value 634.455179
## iter 440 value 633.785330
## iter 450 value 632.290941
## iter 460 value 629.927131
## iter 470 value 627.166726
## iter 480 value 627.104873
## iter 490 value 626.487275
## iter 500 value 622.075383
## final value 622.075383
## stopped after 500 iterations
## # weights: 121
## initial value 1390036.315580
## iter 10 value 2272.500271
## iter 20 value 938.577558
## iter 30 value 764.399625
## iter 40 value 642.698333
## iter 50 value 581.513296
## iter 60 value 525.736839
## iter 70 value 492.709877
## iter 80 value 470.359903
## iter 90 value 449.420348
## iter 100 value 424.244315
## iter 110 value 401.822055
## iter 120 value 381.573619
## iter 130 value 359.346680
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## iter 150 value 339.274110
## iter 160 value 332.838280
## iter 170 value 324.992645
## iter 180 value 317.912289
## iter 190 value 314.029811
## iter 200 value 310.151792
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## iter 220 value 304.928401
## iter 230 value 302.199881
## iter 240 value 299.556713
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## iter 300 value 291.741293
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## iter 400 value 281.353352
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## iter 470 value 280.553348
## iter 480 value 280.439934
## iter 490 value 280.339714
## iter 500 value 280.281032
## final value 280.281032
## stopped after 500 iterations
## # weights: 181
## initial value 1406500.058150
## iter 10 value 1205.902608
## iter 20 value 808.220355
## iter 30 value 650.812361
## iter 40 value 529.300351
## iter 50 value 441.835262
## iter 60 value 382.485628
## iter 70 value 316.382992
## iter 80 value 280.632855
## iter 90 value 260.914881
## iter 100 value 239.610672
## iter 110 value 223.945286
## iter 120 value 202.637381
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## iter 140 value 174.871875
## iter 150 value 165.592737
## iter 160 value 159.621926
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## iter 190 value 140.444637
## iter 200 value 133.862973
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## iter 220 value 124.292003
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## iter 250 value 117.775155
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## iter 500 value 99.567269
## final value 99.567269
## stopped after 500 iterations
## # weights: 241
## initial value 1425732.097986
## iter 10 value 1375.640186
## iter 20 value 821.895031
## iter 30 value 674.939125
## iter 40 value 502.232512
## iter 50 value 429.798325
## iter 60 value 358.629293
## iter 70 value 307.894247
## iter 80 value 271.940674
## iter 90 value 246.675038
## iter 100 value 224.864809
## iter 110 value 209.590616
## iter 120 value 198.214014
## iter 130 value 190.841716
## iter 140 value 184.426557
## iter 150 value 176.574111
## iter 160 value 167.216698
## iter 170 value 159.219820
## iter 180 value 150.289408
## iter 190 value 142.661961
## iter 200 value 136.443674
## iter 210 value 126.965162
## iter 220 value 119.114537
## iter 230 value 113.614611
## iter 240 value 107.524755
## iter 250 value 101.507598
## iter 260 value 97.952182
## iter 270 value 94.091510
## iter 280 value 89.646154
## iter 290 value 85.841245
## iter 300 value 80.774047
## iter 310 value 74.506634
## iter 320 value 70.152405
## iter 330 value 65.298885
## iter 340 value 62.480585
## iter 350 value 59.811508
## iter 360 value 57.588691
## iter 370 value 55.438650
## iter 380 value 53.187944
## iter 390 value 50.253663
## iter 400 value 48.655191
## iter 410 value 46.851427
## iter 420 value 45.727083
## iter 430 value 44.974745
## iter 440 value 44.212167
## iter 450 value 43.374185
## iter 460 value 42.610683
## iter 470 value 42.158281
## iter 480 value 41.509030
## iter 490 value 41.182159
## iter 500 value 41.096494
## final value 41.096494
## stopped after 500 iterations
## # weights: 25
## initial value 1364156.587034
## iter 10 value 19539.964640
## iter 20 value 14785.573082
## iter 30 value 11058.799529
## iter 40 value 3463.995804
## iter 50 value 1678.490273
## iter 60 value 1297.823507
## iter 70 value 1179.016997
## iter 80 value 1173.001288
## iter 90 value 1167.044784
## iter 100 value 1148.292159
## iter 110 value 1147.931182
## final value 1147.930339
## converged
## # weights: 61
## initial value 1414575.668548
## iter 10 value 76283.602962
## iter 20 value 35031.985881
## iter 30 value 21693.498478
## iter 40 value 12984.117480
## iter 50 value 7398.826873
## iter 60 value 6909.962394
## iter 70 value 4436.751243
## iter 80 value 2952.111164
## iter 90 value 2105.011744
## iter 100 value 1449.891408
## iter 110 value 1176.898926
## iter 120 value 1092.502129
## iter 130 value 1031.599327
## iter 140 value 993.858163
## iter 150 value 956.603983
## iter 160 value 930.869144
## iter 170 value 918.121027
## iter 180 value 910.244988
## iter 190 value 892.411503
## iter 200 value 870.429695
## iter 210 value 855.490608
## iter 220 value 849.247674
## iter 230 value 846.339099
## iter 240 value 845.042226
## iter 250 value 844.976353
## iter 260 value 844.972309
## final value 844.971867
## converged
## # weights: 121
## initial value 1398789.313064
## iter 10 value 1245.524635
## iter 20 value 931.859082
## iter 30 value 795.605418
## iter 40 value 704.351808
## iter 50 value 651.861833
## iter 60 value 619.390576
## iter 70 value 592.516517
## iter 80 value 570.088424
## iter 90 value 554.220154
## iter 100 value 540.937813
## iter 110 value 528.404300
## iter 120 value 518.983216
## iter 130 value 509.965185
## iter 140 value 508.015754
## iter 150 value 506.145222
## iter 160 value 504.767414
## iter 170 value 502.899432
## iter 180 value 501.342660
## iter 190 value 498.254652
## iter 200 value 492.867182
## iter 210 value 489.031889
## iter 220 value 486.915882
## iter 230 value 485.663998
## iter 240 value 482.233454
## iter 250 value 479.707872
## iter 260 value 477.795332
## iter 270 value 474.942479
## iter 280 value 472.789943
## iter 290 value 468.435254
## iter 300 value 463.322175
## iter 310 value 460.870158
## iter 320 value 458.411777
## iter 330 value 456.307730
## iter 340 value 454.858027
## iter 350 value 452.418812
## iter 360 value 450.989034
## iter 370 value 450.433488
## iter 380 value 450.092757
## iter 390 value 449.746967
## iter 400 value 449.615295
## iter 410 value 449.575150
## iter 420 value 449.560120
## iter 430 value 449.557328
## final value 449.557048
## converged
## # weights: 181
## initial value 1346723.388655
## iter 10 value 1101.374678
## iter 20 value 864.999899
## iter 30 value 739.426692
## iter 40 value 650.496874
## iter 50 value 588.301452
## iter 60 value 555.138072
## iter 70 value 522.899716
## iter 80 value 497.945826
## iter 90 value 484.694735
## iter 100 value 469.009760
## iter 110 value 455.802350
## iter 120 value 449.051663
## iter 130 value 443.844046
## iter 140 value 439.253970
## iter 150 value 435.087032
## iter 160 value 431.127004
## iter 170 value 426.974663
## iter 180 value 420.786428
## iter 190 value 416.027188
## iter 200 value 412.809534
## iter 210 value 409.604306
## iter 220 value 404.630504
## iter 230 value 400.436890
## iter 240 value 395.725722
## iter 250 value 393.608461
## iter 260 value 390.758111
## iter 270 value 387.528131
## iter 280 value 381.836783
## iter 290 value 379.516696
## iter 300 value 376.297230
## iter 310 value 372.710206
## iter 320 value 369.615908
## iter 330 value 366.944220
## iter 340 value 365.069726
## iter 350 value 363.283517
## iter 360 value 361.330166
## iter 370 value 360.345732
## iter 380 value 359.371164
## iter 390 value 357.009300
## iter 400 value 355.587019
## iter 410 value 354.970429
## iter 420 value 354.070544
## iter 430 value 353.282444
## iter 440 value 352.916982
## iter 450 value 352.650010
## iter 460 value 352.239208
## iter 470 value 351.409474
## iter 480 value 350.727397
## iter 490 value 350.492440
## iter 500 value 350.430458
## final value 350.430458
## stopped after 500 iterations
## # weights: 241
## initial value 1375346.489162
## iter 10 value 1367.781777
## iter 20 value 952.422485
## iter 30 value 744.106819
## iter 40 value 640.112622
## iter 50 value 580.162631
## iter 60 value 534.425015
## iter 70 value 506.890845
## iter 80 value 481.737665
## iter 90 value 463.292341
## iter 100 value 449.669845
## iter 110 value 444.419172
## iter 120 value 439.458936
## iter 130 value 434.693792
## iter 140 value 429.813411
## iter 150 value 424.204475
## iter 160 value 417.521980
## iter 170 value 410.775121
## iter 180 value 402.844156
## iter 190 value 397.131327
## iter 200 value 393.128716
## iter 210 value 389.588363
## iter 220 value 385.937226
## iter 230 value 382.106459
## iter 240 value 379.076241
## iter 250 value 375.895933
## iter 260 value 372.705938
## iter 270 value 368.916919
## iter 280 value 366.265915
## iter 290 value 361.644001
## iter 300 value 357.251667
## iter 310 value 354.556305
## iter 320 value 352.597746
## iter 330 value 351.230341
## iter 340 value 349.928834
## iter 350 value 348.678311
## iter 360 value 347.004868
## iter 370 value 344.336915
## iter 380 value 342.492997
## iter 390 value 340.550433
## iter 400 value 339.090973
## iter 410 value 337.914670
## iter 420 value 337.158985
## iter 430 value 336.342028
## iter 440 value 335.526791
## iter 450 value 334.512118
## iter 460 value 333.043500
## iter 470 value 330.402574
## iter 480 value 327.984257
## iter 490 value 326.813734
## iter 500 value 325.843866
## final value 325.843866
## stopped after 500 iterations
## # weights: 25
## initial value 1394525.925872
## iter 10 value 96591.768306
## iter 20 value 16831.014352
## iter 30 value 15384.630985
## iter 40 value 15083.243450
## iter 50 value 15075.854447
## iter 60 value 15072.182372
## iter 70 value 15065.526987
## iter 80 value 12120.845998
## iter 90 value 8838.201624
## iter 100 value 5509.937832
## iter 110 value 5483.839417
## iter 120 value 5438.417239
## iter 130 value 5380.122125
## iter 140 value 4735.464660
## iter 150 value 1628.060636
## iter 160 value 1453.620316
## iter 170 value 1388.298703
## iter 180 value 1372.723301
## iter 190 value 1367.740539
## iter 200 value 1339.280814
## iter 210 value 1280.134873
## iter 220 value 1276.379470
## iter 230 value 1255.258570
## iter 240 value 1254.302727
## iter 250 value 1253.854252
## iter 260 value 1253.847075
## iter 270 value 1241.281215
## iter 280 value 1232.996042
## iter 290 value 1232.646939
## iter 300 value 1216.368372
## iter 310 value 1207.771213
## iter 320 value 1205.549531
## iter 330 value 1198.297716
## iter 340 value 1181.254359
## iter 350 value 1177.466464
## iter 360 value 1167.810985
## iter 370 value 1167.158281
## final value 1167.157437
## converged
## # weights: 61
## initial value 1382301.841007
## iter 10 value 197526.920235
## iter 20 value 9635.408165
## iter 30 value 4792.720309
## iter 40 value 3680.483923
## iter 50 value 2423.108187
## iter 60 value 1652.898777
## iter 70 value 1281.654353
## iter 80 value 1167.694244
## iter 90 value 1123.592482
## iter 100 value 1061.404972
## iter 110 value 904.521784
## iter 120 value 837.800852
## iter 130 value 804.907375
## iter 140 value 775.416852
## iter 150 value 754.587690
## iter 160 value 747.637225
## iter 170 value 729.189097
## iter 180 value 728.253221
## iter 190 value 724.846727
## iter 200 value 714.302200
## iter 210 value 710.181162
## iter 220 value 705.381564
## iter 230 value 700.195031
## iter 240 value 697.287720
## iter 250 value 694.076784
## iter 260 value 688.137707
## iter 270 value 686.072856
## iter 280 value 682.652251
## iter 290 value 676.285247
## iter 300 value 658.730408
## iter 310 value 644.137320
## iter 320 value 639.347843
## iter 330 value 636.097032
## iter 340 value 633.707720
## iter 350 value 632.936031
## iter 360 value 632.581748
## iter 370 value 632.261613
## iter 380 value 631.725310
## iter 390 value 631.661774
## iter 400 value 631.655817
## iter 410 value 631.578135
## iter 420 value 631.372563
## iter 430 value 631.095213
## iter 440 value 630.700405
## iter 450 value 625.254516
## iter 460 value 620.312404
## iter 470 value 618.656560
## iter 480 value 617.849330
## iter 490 value 617.604580
## iter 500 value 617.529045
## final value 617.529045
## stopped after 500 iterations
## # weights: 121
## initial value 1409867.375222
## iter 10 value 1686.405311
## iter 20 value 857.985567
## iter 30 value 660.476272
## iter 40 value 600.954001
## iter 50 value 527.974385
## iter 60 value 470.255145
## iter 70 value 436.553263
## iter 80 value 408.015208
## iter 90 value 390.910751
## iter 100 value 377.906162
## iter 110 value 358.840807
## iter 120 value 345.128001
## iter 130 value 341.036040
## iter 140 value 338.499667
## iter 150 value 326.182967
## iter 160 value 323.129776
## iter 170 value 321.399314
## iter 180 value 318.828754
## iter 190 value 316.899890
## iter 200 value 311.504787
## iter 210 value 308.387322
## iter 220 value 305.132693
## iter 230 value 302.722422
## iter 240 value 300.499990
## iter 250 value 299.968106
## iter 260 value 299.416763
## iter 270 value 298.681857
## iter 280 value 297.886601
## iter 290 value 296.718652
## iter 300 value 295.772848
## iter 310 value 294.240028
## iter 320 value 292.666977
## iter 330 value 291.284409
## iter 340 value 290.598466
## iter 350 value 290.141568
## iter 360 value 289.676749
## iter 370 value 289.432865
## iter 380 value 289.323966
## iter 390 value 289.283227
## iter 400 value 289.256618
## iter 410 value 289.231913
## iter 420 value 289.206883
## iter 430 value 289.196671
## iter 440 value 289.192607
## iter 450 value 289.190660
## iter 460 value 289.189607
## iter 470 value 289.189377
## final value 289.189261
## converged
## # weights: 181
## initial value 1374732.323372
## iter 10 value 993.897666
## iter 20 value 798.551150
## iter 30 value 632.626272
## iter 40 value 516.047781
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## final value 133.541902
## stopped after 500 iterations
## # weights: 241
## initial value 1420191.736214
## iter 10 value 2057.734051
## iter 20 value 894.692453
## iter 30 value 685.520099
## iter 40 value 536.892384
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## final value 56.443051
## stopped after 500 iterations
## # weights: 25
## initial value 1398875.043009
## iter 10 value 8819.870440
## iter 20 value 5882.813063
## iter 30 value 5507.897629
## iter 40 value 5451.451290
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## iter 480 value 1062.196960
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## iter 500 value 1017.431136
## final value 1017.431136
## stopped after 500 iterations
## # weights: 61
## initial value 1383047.417602
## iter 10 value 16099.374415
## iter 20 value 11548.531358
## iter 30 value 10546.615096
## iter 40 value 7370.107431
## iter 50 value 3578.066733
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## iter 490 value 586.424850
## iter 500 value 586.421392
## final value 586.421392
## stopped after 500 iterations
## # weights: 121
## initial value 1370582.836008
## iter 10 value 3631.614799
## iter 20 value 1461.785218
## iter 30 value 937.099507
## iter 40 value 726.762550
## iter 50 value 639.689765
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## iter 100 value 483.930230
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## iter 500 value 270.540563
## final value 270.540563
## stopped after 500 iterations
## # weights: 181
## initial value 1434633.464253
## iter 10 value 1211.061852
## iter 20 value 740.764855
## iter 30 value 616.856084
## iter 40 value 503.382613
## iter 50 value 410.970976
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## iter 90 value 276.229714
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## iter 500 value 80.021326
## final value 80.021326
## stopped after 500 iterations
## # weights: 241
## initial value 1380919.724748
## iter 10 value 1184.693603
## iter 20 value 789.846203
## iter 30 value 626.099619
## iter 40 value 513.622058
## iter 50 value 405.533747
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## iter 80 value 275.686075
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## iter 480 value 47.552507
## iter 490 value 47.350896
## iter 500 value 47.315523
## final value 47.315523
## stopped after 500 iterations
## # weights: 25
## initial value 1381291.702288
## iter 10 value 19170.296626
## iter 20 value 10929.812398
## iter 30 value 7897.891611
## iter 40 value 6679.587140
## iter 50 value 6334.766106
## iter 60 value 6013.615421
## iter 70 value 5780.300317
## iter 80 value 5769.670774
## iter 90 value 5748.883112
## iter 100 value 4648.913394
## iter 110 value 3996.047390
## iter 120 value 3018.082634
## iter 130 value 1460.402361
## iter 140 value 1232.551252
## iter 150 value 1195.956300
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## iter 260 value 1141.040413
## iter 270 value 1139.757698
## iter 280 value 1139.181993
## iter 290 value 1139.143473
## final value 1139.143279
## converged
## # weights: 61
## initial value 1419395.360927
## iter 10 value 186509.571527
## iter 20 value 7215.249148
## iter 30 value 5607.241744
## iter 40 value 5032.049500
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## iter 60 value 4088.247275
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## iter 100 value 3809.492153
## iter 110 value 3622.428748
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## iter 160 value 1382.820839
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## iter 200 value 940.005258
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## iter 220 value 881.033606
## iter 230 value 872.761878
## iter 240 value 868.823722
## iter 250 value 861.873725
## iter 260 value 855.749084
## iter 270 value 844.754110
## iter 280 value 826.804347
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## iter 300 value 816.018365
## iter 310 value 814.807838
## iter 320 value 812.014330
## iter 330 value 809.141530
## iter 340 value 806.424473
## iter 350 value 800.620058
## iter 360 value 794.993656
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## iter 390 value 783.201729
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## iter 460 value 727.764487
## iter 470 value 717.041868
## iter 480 value 713.670614
## iter 490 value 708.424507
## iter 500 value 703.601768
## final value 703.601768
## stopped after 500 iterations
## # weights: 121
## initial value 1429577.952893
## iter 10 value 2390.312660
## iter 20 value 1072.965842
## iter 30 value 818.206099
## iter 40 value 735.713867
## iter 50 value 699.178663
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## iter 70 value 612.585915
## iter 80 value 574.217161
## iter 90 value 521.422068
## iter 100 value 491.239908
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## iter 120 value 459.193485
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## iter 200 value 375.879126
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## iter 280 value 363.935004
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## iter 300 value 363.810366
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## iter 320 value 363.617918
## iter 330 value 363.599836
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## iter 470 value 363.067940
## iter 480 value 363.057312
## iter 490 value 363.031294
## iter 500 value 362.991097
## final value 362.991097
## stopped after 500 iterations
## # weights: 181
## initial value 1350755.748260
## iter 10 value 1220.402385
## iter 20 value 763.100713
## iter 30 value 633.896971
## iter 40 value 466.452598
## iter 50 value 399.771149
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## iter 80 value 293.813323
## iter 90 value 271.838244
## iter 100 value 254.766132
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## iter 130 value 216.780280
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## iter 150 value 193.601419
## iter 160 value 187.790115
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## iter 180 value 175.460822
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## iter 240 value 135.812465
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## iter 300 value 115.152527
## iter 310 value 112.582094
## iter 320 value 110.277962
## iter 330 value 108.767532
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## iter 350 value 103.695158
## iter 360 value 101.830291
## iter 370 value 100.757616
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## iter 460 value 86.046780
## iter 470 value 81.820142
## iter 480 value 76.655161
## iter 490 value 74.604135
## iter 500 value 72.456178
## final value 72.456178
## stopped after 500 iterations
## # weights: 241
## initial value 1362495.416274
## iter 10 value 1158.839226
## iter 20 value 766.560537
## iter 30 value 623.058648
## iter 40 value 514.301309
## iter 50 value 427.288347
## iter 60 value 347.916089
## iter 70 value 307.047745
## iter 80 value 273.553583
## iter 90 value 254.909816
## iter 100 value 227.994250
## iter 110 value 202.027139
## iter 120 value 185.216303
## iter 130 value 171.301744
## iter 140 value 161.649326
## iter 150 value 154.450584
## iter 160 value 147.610851
## iter 170 value 140.130007
## iter 180 value 133.286893
## iter 190 value 124.767430
## iter 200 value 115.677226
## iter 210 value 106.998846
## iter 220 value 98.463812
## iter 230 value 93.007201
## iter 240 value 86.658881
## iter 250 value 82.328241
## iter 260 value 79.060513
## iter 270 value 76.524798
## iter 280 value 73.733815
## iter 290 value 70.893231
## iter 300 value 68.572904
## iter 310 value 66.444971
## iter 320 value 64.926784
## iter 330 value 62.693781
## iter 340 value 60.601489
## iter 350 value 58.470096
## iter 360 value 56.653475
## iter 370 value 54.338856
## iter 380 value 52.034366
## iter 390 value 49.972294
## iter 400 value 47.833071
## iter 410 value 46.014835
## iter 420 value 43.722277
## iter 430 value 41.594130
## iter 440 value 39.594202
## iter 450 value 37.415070
## iter 460 value 35.317576
## iter 470 value 34.456194
## iter 480 value 33.682148
## iter 490 value 33.396856
## iter 500 value 33.290554
## final value 33.290554
## stopped after 500 iterations
## # weights: 25
## initial value 1352804.101603
## iter 10 value 7275.747305
## iter 20 value 5671.697933
## iter 30 value 4972.380138
## iter 40 value 3179.032765
## iter 50 value 1704.377016
## iter 60 value 1450.697286
## iter 70 value 1432.154930
## iter 80 value 1354.504384
## iter 90 value 1336.069149
## iter 100 value 1329.949531
## iter 110 value 1327.451469
## iter 120 value 1327.351137
## iter 130 value 1325.226789
## iter 140 value 1323.396121
## iter 150 value 1322.155002
## iter 160 value 1321.358865
## iter 170 value 1321.326107
## final value 1321.326029
## converged
## # weights: 61
## initial value 1381519.965639
## iter 10 value 156745.089010
## iter 20 value 19165.135566
## iter 30 value 4707.429300
## iter 40 value 2910.576358
## iter 50 value 2507.757296
## iter 60 value 2442.203789
## iter 70 value 2400.313853
## iter 80 value 2392.664782
## iter 90 value 2380.635613
## iter 100 value 2325.955800
## iter 110 value 2199.569931
## iter 120 value 1817.425136
## iter 130 value 1391.646552
## iter 140 value 1254.540213
## iter 150 value 1219.271583
## iter 160 value 1198.840266
## iter 170 value 1174.080006
## iter 180 value 1172.475059
## iter 190 value 1171.034220
## iter 200 value 1170.555707
## iter 210 value 1170.345513
## iter 220 value 1170.212457
## final value 1170.212293
## converged
## # weights: 121
## initial value 1413421.109585
## iter 10 value 1164.292169
## iter 20 value 917.817139
## iter 30 value 794.873953
## iter 40 value 704.864810
## iter 50 value 645.498750
## iter 60 value 599.702107
## iter 70 value 534.997492
## iter 80 value 502.120381
## iter 90 value 474.354445
## iter 100 value 456.554333
## iter 110 value 429.374554
## iter 120 value 393.297491
## iter 130 value 370.217467
## iter 140 value 351.341173
## iter 150 value 339.785544
## iter 160 value 331.455091
## iter 170 value 321.697691
## iter 180 value 310.101631
## iter 190 value 297.704618
## iter 200 value 288.833757
## iter 210 value 279.004290
## iter 220 value 271.420859
## iter 230 value 266.658103
## iter 240 value 261.757831
## iter 250 value 258.717112
## iter 260 value 257.455015
## iter 270 value 256.695570
## iter 280 value 254.898675
## iter 290 value 253.070971
## iter 300 value 251.184672
## iter 310 value 249.060118
## iter 320 value 246.855712
## iter 330 value 244.456969
## iter 340 value 241.698852
## iter 350 value 239.301865
## iter 360 value 236.819552
## iter 370 value 235.800689
## iter 380 value 235.176343
## iter 390 value 234.545566
## iter 400 value 234.206551
## iter 410 value 233.947747
## iter 420 value 233.490559
## iter 430 value 232.665740
## iter 440 value 231.071099
## iter 450 value 229.779929
## iter 460 value 226.675373
## iter 470 value 225.458197
## iter 480 value 224.716210
## iter 490 value 224.045071
## iter 500 value 223.982740
## final value 223.982740
## stopped after 500 iterations
## # weights: 181
## initial value 1401015.855766
## iter 10 value 1199.275633
## iter 20 value 835.197898
## iter 30 value 667.544267
## iter 40 value 529.879974
## iter 50 value 442.998801
## iter 60 value 394.055870
## iter 70 value 339.969879
## iter 80 value 309.182838
## iter 90 value 282.462925
## iter 100 value 261.871797
## iter 110 value 246.757338
## iter 120 value 233.521851
## iter 130 value 225.709182
## iter 140 value 216.937743
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## iter 170 value 189.384304
## iter 180 value 182.776166
## iter 190 value 174.835674
## iter 200 value 169.414834
## iter 210 value 165.400080
## iter 220 value 162.083955
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## iter 260 value 147.163452
## iter 270 value 144.798286
## iter 280 value 141.196879
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## iter 310 value 136.166298
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## iter 370 value 116.948100
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## iter 400 value 115.089880
## iter 410 value 114.442252
## iter 420 value 114.124754
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## iter 460 value 112.293287
## iter 470 value 111.870424
## iter 480 value 110.661587
## iter 490 value 107.888475
## iter 500 value 105.202963
## final value 105.202963
## stopped after 500 iterations
## # weights: 241
## initial value 1347790.223486
## iter 10 value 1274.694409
## iter 20 value 808.576311
## iter 30 value 648.638270
## iter 40 value 536.287785
## iter 50 value 385.116096
## iter 60 value 318.603221
## iter 70 value 260.793058
## iter 80 value 221.822935
## iter 90 value 192.716942
## iter 100 value 173.013705
## iter 110 value 155.943760
## iter 120 value 141.480877
## iter 130 value 132.621339
## iter 140 value 125.795299
## iter 150 value 118.299518
## iter 160 value 108.361337
## iter 170 value 99.629122
## iter 180 value 94.671734
## iter 190 value 90.431288
## iter 200 value 87.952221
## iter 210 value 85.894707
## iter 220 value 83.802836
## iter 230 value 81.958632
## iter 240 value 79.765769
## iter 250 value 77.144538
## iter 260 value 74.585932
## iter 270 value 73.050789
## iter 280 value 71.046833
## iter 290 value 69.264372
## iter 300 value 66.930977
## iter 310 value 64.682696
## iter 320 value 62.481003
## iter 330 value 59.675131
## iter 340 value 57.580304
## iter 350 value 55.487808
## iter 360 value 53.851191
## iter 370 value 52.370625
## iter 380 value 50.969494
## iter 390 value 49.219205
## iter 400 value 48.093818
## iter 410 value 47.048995
## iter 420 value 45.992675
## iter 430 value 44.750350
## iter 440 value 43.802829
## iter 450 value 42.887897
## iter 460 value 41.846880
## iter 470 value 41.190365
## iter 480 value 40.540935
## iter 490 value 40.315329
## iter 500 value 40.266483
## final value 40.266483
## stopped after 500 iterations
## # weights: 25
## initial value 1401025.775953
## iter 10 value 11043.404590
## iter 20 value 6905.338699
## iter 30 value 5070.199120
## iter 40 value 3761.324029
## iter 50 value 2309.328186
## iter 60 value 1791.106287
## iter 70 value 1480.559083
## iter 80 value 1386.729777
## iter 90 value 1307.764525
## iter 100 value 1215.603654
## iter 110 value 1178.236964
## iter 120 value 1176.015066
## iter 130 value 1175.123844
## iter 140 value 1172.427184
## final value 1172.395952
## converged
## # weights: 61
## initial value 1404180.903774
## iter 10 value 31573.502817
## iter 20 value 13231.596346
## iter 30 value 7399.407160
## iter 40 value 5159.434897
## iter 50 value 4497.743774
## iter 60 value 3930.768539
## iter 70 value 3124.321637
## iter 80 value 2439.457839
## iter 90 value 2009.434616
## iter 100 value 1573.486539
## iter 110 value 1357.006407
## iter 120 value 1140.361910
## iter 130 value 1019.760235
## iter 140 value 963.360855
## iter 150 value 930.098693
## iter 160 value 908.354357
## iter 170 value 888.272008
## iter 180 value 881.296389
## iter 190 value 876.695459
## iter 200 value 874.615451
## iter 210 value 870.363023
## iter 220 value 838.470595
## iter 230 value 835.049599
## iter 240 value 833.398671
## iter 250 value 831.916336
## iter 260 value 831.420624
## iter 270 value 831.317426
## iter 280 value 831.257805
## final value 831.255576
## converged
## # weights: 121
## initial value 1370782.628932
## iter 10 value 1835.185494
## iter 20 value 1182.742831
## iter 30 value 891.547420
## iter 40 value 816.593527
## iter 50 value 743.507320
## iter 60 value 698.062941
## iter 70 value 671.555344
## iter 80 value 655.315633
## iter 90 value 637.368157
## iter 100 value 621.149274
## iter 110 value 605.459192
## iter 120 value 591.140128
## iter 130 value 582.825726
## iter 140 value 574.736895
## iter 150 value 569.321025
## iter 160 value 565.440523
## iter 170 value 561.799353
## iter 180 value 558.674189
## iter 190 value 557.034366
## iter 200 value 555.502303
## iter 210 value 553.398124
## iter 220 value 551.024506
## iter 230 value 545.203907
## iter 240 value 540.828656
## iter 250 value 539.351522
## iter 260 value 537.898756
## iter 270 value 535.629435
## iter 280 value 533.517504
## iter 290 value 531.924612
## iter 300 value 531.438415
## iter 310 value 531.235964
## iter 320 value 531.033193
## iter 330 value 530.879238
## iter 340 value 530.853299
## iter 350 value 530.849633
## final value 530.849296
## converged
## # weights: 181
## initial value 1396669.835293
## iter 10 value 1459.249337
## iter 20 value 943.847268
## iter 30 value 764.218153
## iter 40 value 688.685007
## iter 50 value 639.392328
## iter 60 value 590.900548
## iter 70 value 547.721395
## iter 80 value 517.740560
## iter 90 value 500.794098
## iter 100 value 490.518404
## iter 110 value 482.730854
## iter 120 value 476.569548
## iter 130 value 468.086292
## iter 140 value 462.251333
## iter 150 value 455.060492
## iter 160 value 449.760458
## iter 170 value 445.160487
## iter 180 value 437.194820
## iter 190 value 432.797278
## iter 200 value 430.241693
## iter 210 value 428.642115
## iter 220 value 426.887464
## iter 230 value 425.414867
## iter 240 value 423.880808
## iter 250 value 423.040186
## iter 260 value 422.280217
## iter 270 value 421.240249
## iter 280 value 420.300563
## iter 290 value 419.523383
## iter 300 value 418.868494
## iter 310 value 418.280813
## iter 320 value 417.845368
## iter 330 value 417.204688
## iter 340 value 416.610410
## iter 350 value 416.282281
## iter 360 value 416.137726
## iter 370 value 416.057895
## iter 380 value 415.878383
## iter 390 value 415.666092
## iter 400 value 415.582978
## iter 410 value 415.530885
## iter 420 value 415.495366
## iter 430 value 415.486320
## iter 440 value 415.483473
## iter 450 value 415.482621
## final value 415.482481
## converged
## # weights: 241
## initial value 1352823.074906
## iter 10 value 1364.247391
## iter 20 value 944.548855
## iter 30 value 767.862014
## iter 40 value 652.839381
## iter 50 value 551.724887
## iter 60 value 499.355724
## iter 70 value 473.834882
## iter 80 value 452.820290
## iter 90 value 430.623398
## iter 100 value 414.948774
## iter 110 value 406.391932
## iter 120 value 398.320012
## iter 130 value 392.456334
## iter 140 value 387.564611
## iter 150 value 384.748525
## iter 160 value 380.359562
## iter 170 value 376.127466
## iter 180 value 372.548326
## iter 190 value 370.233155
## iter 200 value 368.112323
## iter 210 value 365.701135
## iter 220 value 362.817130
## iter 230 value 360.203970
## iter 240 value 357.282967
## iter 250 value 355.076701
## iter 260 value 353.457357
## iter 270 value 352.071116
## iter 280 value 350.532779
## iter 290 value 349.377216
## iter 300 value 348.477559
## iter 310 value 347.176138
## iter 320 value 345.889497
## iter 330 value 344.609580
## iter 340 value 343.327035
## iter 350 value 342.083699
## iter 360 value 340.756455
## iter 370 value 339.689681
## iter 380 value 338.793105
## iter 390 value 338.143706
## iter 400 value 337.526551
## iter 410 value 336.930599
## iter 420 value 336.412128
## iter 430 value 336.019886
## iter 440 value 335.723649
## iter 450 value 335.419502
## iter 460 value 335.105713
## iter 470 value 334.712651
## iter 480 value 334.293680
## iter 490 value 334.134493
## iter 500 value 334.053183
## final value 334.053183
## stopped after 500 iterations
## # weights: 25
## initial value 1371501.393570
## iter 10 value 15812.800109
## iter 20 value 14498.852704
## iter 30 value 11509.286606
## iter 40 value 5363.631713
## iter 50 value 3885.058771
## iter 60 value 1717.533250
## iter 70 value 1478.854121
## iter 80 value 1390.036701
## iter 90 value 1360.228724
## iter 100 value 1349.258718
## iter 110 value 1337.154358
## iter 120 value 1336.573294
## iter 130 value 1333.436117
## iter 140 value 1319.265144
## iter 150 value 1239.333849
## iter 160 value 1201.120240
## iter 170 value 1169.830973
## iter 180 value 1165.223702
## iter 190 value 1165.175369
## iter 200 value 1165.173977
## iter 210 value 1165.103978
## iter 220 value 1165.044360
## iter 230 value 1165.033940
## final value 1165.033793
## converged
## # weights: 61
## initial value 1367512.781787
## iter 10 value 5624.713892
## iter 20 value 3454.159861
## iter 30 value 3252.868654
## iter 40 value 3123.240549
## iter 50 value 3070.193976
## iter 60 value 3028.540492
## iter 70 value 2922.773532
## iter 80 value 2758.392306
## iter 90 value 2498.258960
## iter 100 value 2149.398314
## iter 110 value 1417.310793
## iter 120 value 1278.139285
## iter 130 value 1234.033903
## iter 140 value 1217.111818
## iter 150 value 1209.421563
## iter 160 value 1168.270417
## iter 170 value 1069.780277
## iter 180 value 886.425343
## iter 190 value 872.826472
## iter 200 value 870.469876
## iter 210 value 869.489018
## iter 220 value 865.489005
## iter 230 value 861.376263
## iter 240 value 860.131048
## iter 250 value 858.754564
## iter 260 value 849.843282
## iter 270 value 730.491024
## iter 280 value 698.628076
## iter 290 value 685.949407
## iter 300 value 683.852151
## iter 310 value 683.489410
## iter 320 value 670.482992
## iter 330 value 662.575406
## iter 340 value 654.561138
## iter 350 value 653.429103
## iter 360 value 652.495234
## iter 370 value 651.839099
## iter 380 value 651.237519
## iter 390 value 646.601274
## iter 400 value 646.108265
## iter 410 value 646.037710
## iter 420 value 646.008244
## iter 430 value 645.991714
## iter 440 value 645.986599
## final value 645.986347
## converged
## # weights: 121
## initial value 1400728.429412
## iter 10 value 1364.706513
## iter 20 value 874.888205
## iter 30 value 768.707568
## iter 40 value 616.102243
## iter 50 value 540.095203
## iter 60 value 472.917485
## iter 70 value 441.599159
## iter 80 value 416.822087
## iter 90 value 403.795130
## iter 100 value 396.867524
## iter 110 value 388.455958
## iter 120 value 380.492912
## iter 130 value 371.013711
## iter 140 value 365.262408
## iter 150 value 360.761071
## iter 160 value 355.991708
## iter 170 value 352.402335
## iter 180 value 349.361904
## iter 190 value 347.035457
## iter 200 value 343.100514
## iter 210 value 336.608525
## iter 220 value 332.409887
## iter 230 value 324.236699
## iter 240 value 319.264667
## iter 250 value 316.547188
## iter 260 value 315.580287
## iter 270 value 314.429761
## iter 280 value 313.350681
## iter 290 value 310.104594
## iter 300 value 303.564755
## iter 310 value 296.815682
## iter 320 value 294.366213
## iter 330 value 291.735325
## iter 340 value 288.748958
## iter 350 value 286.346441
## iter 360 value 284.485142
## iter 370 value 282.677060
## iter 380 value 280.912126
## iter 390 value 279.239437
## iter 400 value 274.843544
## iter 410 value 272.397750
## iter 420 value 271.825284
## iter 430 value 271.346374
## iter 440 value 271.102669
## iter 450 value 270.982241
## iter 460 value 270.863812
## iter 470 value 270.762054
## iter 480 value 270.516961
## iter 490 value 270.194121
## iter 500 value 270.155559
## final value 270.155559
## stopped after 500 iterations
## # weights: 181
## initial value 1355913.169803
## iter 10 value 1214.270392
## iter 20 value 883.564134
## iter 30 value 700.887629
## iter 40 value 540.335990
## iter 50 value 421.797467
## iter 60 value 369.981514
## iter 70 value 331.587285
## iter 80 value 286.309146
## iter 90 value 245.498765
## iter 100 value 220.977403
## iter 110 value 208.209116
## iter 120 value 202.022162
## iter 130 value 193.832690
## iter 140 value 188.154214
## iter 150 value 179.154988
## iter 160 value 171.998335
## iter 170 value 165.404896
## iter 180 value 159.495857
## iter 190 value 153.729249
## iter 200 value 147.000568
## iter 210 value 142.688367
## iter 220 value 139.452120
## iter 230 value 137.143433
## iter 240 value 135.120110
## iter 250 value 133.111719
## iter 260 value 131.704885
## iter 270 value 130.268911
## iter 280 value 129.509212
## iter 290 value 128.682337
## iter 300 value 128.188186
## iter 310 value 127.838772
## iter 320 value 127.587913
## iter 330 value 127.392050
## iter 340 value 127.264736
## iter 350 value 127.089484
## iter 360 value 126.644431
## iter 370 value 126.328975
## iter 380 value 126.153411
## iter 390 value 125.990784
## iter 400 value 125.892351
## iter 410 value 125.656004
## iter 420 value 125.241357
## iter 430 value 124.689348
## iter 440 value 123.753283
## iter 450 value 122.737844
## iter 460 value 121.731978
## iter 470 value 120.928311
## iter 480 value 120.271322
## iter 490 value 119.749593
## iter 500 value 119.341751
## final value 119.341751
## stopped after 500 iterations
## # weights: 241
## initial value 1437169.794289
## iter 10 value 1017.666682
## iter 20 value 778.544960
## iter 30 value 656.124445
## iter 40 value 532.577654
## iter 50 value 407.825427
## iter 60 value 333.251616
## iter 70 value 275.905876
## iter 80 value 240.902671
## iter 90 value 221.447792
## iter 100 value 199.164324
## iter 110 value 189.348405
## iter 120 value 176.492271
## iter 130 value 160.507105
## iter 140 value 146.923815
## iter 150 value 137.611776
## iter 160 value 130.714043
## iter 170 value 125.245865
## iter 180 value 120.551059
## iter 190 value 110.513525
## iter 200 value 104.982509
## iter 210 value 101.253721
## iter 220 value 98.973048
## iter 230 value 96.514495
## iter 240 value 93.610983
## iter 250 value 92.037124
## iter 260 value 90.675025
## iter 270 value 89.387361
## iter 280 value 87.703892
## iter 290 value 86.005334
## iter 300 value 84.452994
## iter 310 value 82.973975
## iter 320 value 81.251950
## iter 330 value 79.557965
## iter 340 value 77.728472
## iter 350 value 75.580478
## iter 360 value 73.771499
## iter 370 value 72.258401
## iter 380 value 70.581541
## iter 390 value 68.984386
## iter 400 value 67.475339
## iter 410 value 66.248227
## iter 420 value 64.570588
## iter 430 value 63.618426
## iter 440 value 62.963411
## iter 450 value 62.086356
## iter 460 value 61.403943
## iter 470 value 60.815296
## iter 480 value 60.207031
## iter 490 value 59.977514
## iter 500 value 59.866257
## final value 59.866257
## stopped after 500 iterations
## # weights: 25
## initial value 1377807.125975
## iter 10 value 6571.488159
## iter 20 value 5474.299869
## iter 30 value 5054.000702
## iter 40 value 4766.021984
## iter 50 value 4441.514471
## iter 60 value 3812.233641
## iter 70 value 1682.001345
## iter 80 value 1352.523134
## iter 90 value 1265.068886
## iter 100 value 1260.123184
## iter 110 value 1239.757906
## iter 120 value 1190.511134
## iter 130 value 1169.493519
## iter 140 value 1166.791414
## iter 150 value 1166.115966
## iter 160 value 1163.250475
## iter 170 value 1112.908266
## iter 180 value 1052.066823
## iter 190 value 959.498259
## iter 200 value 938.049932
## iter 210 value 931.818178
## iter 220 value 929.767684
## iter 230 value 929.584448
## iter 240 value 927.740950
## iter 250 value 926.080303
## iter 260 value 924.047051
## iter 270 value 923.330813
## iter 280 value 923.210748
## iter 290 value 922.871932
## iter 300 value 921.748841
## iter 310 value 921.248068
## iter 320 value 921.034681
## iter 330 value 921.031393
## iter 340 value 921.011617
## iter 350 value 920.986458
## iter 360 value 920.932646
## final value 920.931819
## converged
## # weights: 61
## initial value 1394416.279146
## iter 10 value 17208.579417
## iter 20 value 11420.971312
## iter 30 value 8894.743667
## iter 40 value 3894.354700
## iter 50 value 2457.172741
## iter 60 value 1560.736118
## iter 70 value 1432.108140
## iter 80 value 1327.600256
## iter 90 value 1297.485338
## iter 100 value 1272.102930
## iter 110 value 1206.245478
## iter 120 value 1192.445112
## iter 130 value 1120.609875
## iter 140 value 1111.261865
## iter 150 value 1067.033066
## iter 160 value 1000.138062
## iter 170 value 983.643036
## iter 180 value 967.826068
## iter 190 value 957.462638
## iter 200 value 944.098703
## iter 210 value 931.826221
## iter 220 value 926.470247
## iter 230 value 925.423011
## iter 240 value 925.330823
## iter 250 value 921.795980
## iter 260 value 918.060620
## iter 270 value 910.917522
## iter 280 value 875.982628
## iter 290 value 864.935981
## iter 300 value 855.815601
## iter 310 value 847.488670
## iter 320 value 829.575724
## iter 330 value 809.236075
## iter 340 value 798.556319
## iter 350 value 793.713913
## iter 360 value 784.493779
## iter 370 value 755.090620
## iter 380 value 738.768033
## iter 390 value 722.972468
## iter 400 value 710.575390
## iter 410 value 702.027676
## iter 420 value 698.319174
## iter 430 value 698.197091
## iter 440 value 697.300272
## iter 450 value 696.359848
## iter 460 value 694.890755
## iter 470 value 694.256092
## iter 480 value 694.234828
## iter 490 value 693.352075
## iter 500 value 681.434804
## final value 681.434804
## stopped after 500 iterations
## # weights: 121
## initial value 1390362.490798
## iter 10 value 2648.004641
## iter 20 value 1205.686882
## iter 30 value 917.755030
## iter 40 value 800.922374
## iter 50 value 692.218904
## iter 60 value 624.749929
## iter 70 value 588.463821
## iter 80 value 559.316647
## iter 90 value 532.620604
## iter 100 value 481.864839
## iter 110 value 441.655282
## iter 120 value 409.950081
## iter 130 value 389.881490
## iter 140 value 374.135902
## iter 150 value 361.803094
## iter 160 value 343.566365
## iter 170 value 328.360974
## iter 180 value 316.414340
## iter 190 value 310.646083
## iter 200 value 302.806307
## iter 210 value 295.855510
## iter 220 value 291.515606
## iter 230 value 287.724545
## iter 240 value 285.039908
## iter 250 value 282.966321
## iter 260 value 282.345764
## iter 270 value 281.698190
## iter 280 value 280.775431
## iter 290 value 279.881187
## iter 300 value 278.536968
## iter 310 value 275.788688
## iter 320 value 272.457381
## iter 330 value 269.747254
## iter 340 value 266.368074
## iter 350 value 264.436657
## iter 360 value 263.384848
## iter 370 value 262.949770
## iter 380 value 262.069030
## iter 390 value 261.497070
## iter 400 value 261.161976
## iter 410 value 260.863557
## iter 420 value 260.627309
## iter 430 value 260.478044
## iter 440 value 260.426514
## iter 450 value 260.411949
## iter 460 value 260.286238
## iter 470 value 259.842159
## iter 480 value 259.215802
## iter 490 value 258.932330
## iter 500 value 258.863038
## final value 258.863038
## stopped after 500 iterations
## # weights: 181
## initial value 1383869.645027
## iter 10 value 1091.540184
## iter 20 value 806.391746
## iter 30 value 684.300192
## iter 40 value 550.975552
## iter 50 value 462.011598
## iter 60 value 409.366577
## iter 70 value 378.826973
## iter 80 value 333.905667
## iter 90 value 305.722793
## iter 100 value 282.804996
## iter 110 value 263.892580
## iter 120 value 253.456544
## iter 130 value 239.647238
## iter 140 value 226.497078
## iter 150 value 205.807920
## iter 160 value 195.192175
## iter 170 value 186.773190
## iter 180 value 179.471846
## iter 190 value 171.393317
## iter 200 value 164.074367
## iter 210 value 158.300981
## iter 220 value 154.094084
## iter 230 value 150.373402
## iter 240 value 147.369726
## iter 250 value 144.867283
## iter 260 value 142.460081
## iter 270 value 140.767323
## iter 280 value 138.857173
## iter 290 value 137.717805
## iter 300 value 136.309401
## iter 310 value 134.101809
## iter 320 value 131.361328
## iter 330 value 127.308282
## iter 340 value 123.785654
## iter 350 value 121.889568
## iter 360 value 120.962597
## iter 370 value 120.642618
## iter 380 value 120.499033
## iter 390 value 120.330784
## iter 400 value 120.014842
## iter 410 value 119.594534
## iter 420 value 119.046423
## iter 430 value 118.216659
## iter 440 value 117.351317
## iter 450 value 116.225069
## iter 460 value 113.811917
## iter 470 value 110.930996
## iter 480 value 108.557149
## iter 490 value 106.887538
## iter 500 value 104.118412
## final value 104.118412
## stopped after 500 iterations
## # weights: 241
## initial value 1291557.121004
## iter 10 value 1821.909269
## iter 20 value 861.958014
## iter 30 value 662.823465
## iter 40 value 544.939462
## iter 50 value 437.716808
## iter 60 value 314.595553
## iter 70 value 273.299689
## iter 80 value 241.417904
## iter 90 value 212.191286
## iter 100 value 191.243296
## iter 110 value 172.740129
## iter 120 value 156.368282
## iter 130 value 143.913901
## iter 140 value 136.598269
## iter 150 value 130.852371
## iter 160 value 126.962558
## iter 170 value 122.262724
## iter 180 value 116.136330
## iter 190 value 108.954618
## iter 200 value 102.602115
## iter 210 value 96.899967
## iter 220 value 90.876366
## iter 230 value 85.493934
## iter 240 value 81.090770
## iter 250 value 75.938551
## iter 260 value 71.047903
## iter 270 value 67.060618
## iter 280 value 64.242047
## iter 290 value 62.755045
## iter 300 value 60.787180
## iter 310 value 59.112476
## iter 320 value 57.037132
## iter 330 value 55.713575
## iter 340 value 54.571772
## iter 350 value 53.424373
## iter 360 value 52.135964
## iter 370 value 51.249510
## iter 380 value 50.371189
## iter 390 value 49.141751
## iter 400 value 47.525856
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## iter 420 value 45.506862
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## iter 480 value 42.697766
## iter 490 value 42.587830
## iter 500 value 42.555918
## final value 42.555918
## stopped after 500 iterations
## # weights: 25
## initial value 1372579.696316
## iter 10 value 6620.517409
## iter 20 value 5371.553625
## iter 30 value 4710.588378
## iter 40 value 3381.651345
## iter 50 value 2038.840206
## iter 60 value 1502.700589
## iter 70 value 1398.475042
## iter 80 value 1381.993899
## iter 90 value 1377.585427
## iter 100 value 1343.262786
## iter 110 value 1331.907187
## iter 120 value 1327.214622
## iter 130 value 1324.885694
## iter 140 value 1324.447117
## iter 150 value 1323.962569
## iter 160 value 1322.367410
## iter 170 value 1321.739756
## iter 180 value 1321.181113
## iter 190 value 1321.015005
## iter 200 value 1320.965339
## iter 200 value 1320.965326
## iter 210 value 1320.951379
## final value 1320.950668
## converged
## # weights: 61
## initial value 1409265.016599
## iter 10 value 10053.213138
## iter 20 value 7517.004853
## iter 30 value 5843.041449
## iter 40 value 4020.598466
## iter 50 value 2771.229031
## iter 60 value 2043.457888
## iter 70 value 1480.405356
## iter 80 value 1243.951762
## iter 90 value 1039.848519
## iter 100 value 961.668231
## iter 110 value 916.073889
## iter 120 value 874.119902
## iter 130 value 860.379916
## iter 140 value 857.586689
## iter 150 value 840.522382
## iter 160 value 821.732941
## iter 170 value 812.938170
## iter 180 value 801.320859
## iter 190 value 795.407870
## iter 200 value 789.957027
## iter 210 value 786.922462
## iter 220 value 785.041856
## iter 230 value 782.237408
## iter 240 value 779.379164
## iter 250 value 778.291763
## iter 260 value 778.185551
## iter 270 value 777.770082
## iter 280 value 776.082089
## iter 290 value 773.983796
## iter 300 value 772.877835
## iter 310 value 772.390584
## iter 320 value 772.088415
## iter 330 value 771.796312
## iter 340 value 771.644337
## iter 350 value 771.475921
## iter 360 value 771.091682
## iter 370 value 768.154413
## iter 380 value 767.452754
## iter 390 value 766.548742
## iter 400 value 764.436492
## iter 410 value 759.922245
## iter 420 value 758.867103
## iter 430 value 758.382424
## iter 440 value 757.624797
## iter 450 value 757.410811
## iter 460 value 757.241282
## iter 470 value 757.171615
## iter 480 value 756.993092
## iter 490 value 756.651914
## iter 500 value 756.430855
## final value 756.430855
## stopped after 500 iterations
## # weights: 121
## initial value 1348771.231863
## iter 10 value 2153.978531
## iter 20 value 912.344946
## iter 30 value 757.075887
## iter 40 value 686.727619
## iter 50 value 624.326611
## iter 60 value 565.482013
## iter 70 value 516.615567
## iter 80 value 484.318447
## iter 90 value 460.023250
## iter 100 value 435.507731
## iter 110 value 413.719689
## iter 120 value 403.038519
## iter 130 value 392.183088
## iter 140 value 382.396634
## iter 150 value 378.081087
## iter 160 value 373.071054
## iter 170 value 366.675654
## iter 180 value 354.408421
## iter 190 value 341.185902
## iter 200 value 330.546475
## iter 210 value 322.778823
## iter 220 value 315.541504
## iter 230 value 311.442633
## iter 240 value 307.493502
## iter 250 value 306.600469
## iter 260 value 304.655438
## iter 270 value 301.876211
## iter 280 value 300.319028
## iter 290 value 299.356555
## iter 300 value 295.951787
## iter 310 value 293.427358
## iter 320 value 288.773531
## iter 330 value 278.850399
## iter 340 value 271.921014
## iter 350 value 266.329846
## iter 360 value 264.525485
## iter 370 value 263.862706
## iter 380 value 263.527306
## iter 390 value 263.305211
## iter 400 value 263.229136
## iter 410 value 263.126930
## iter 420 value 262.700327
## iter 430 value 259.663682
## iter 440 value 257.593203
## iter 450 value 256.158937
## iter 460 value 255.067427
## iter 470 value 254.534677
## iter 480 value 254.144963
## iter 490 value 254.047787
## iter 500 value 254.016384
## final value 254.016384
## stopped after 500 iterations
## # weights: 181
## initial value 1430869.219900
## iter 10 value 1203.169298
## iter 20 value 859.977069
## iter 30 value 702.674053
## iter 40 value 569.964810
## iter 50 value 442.915014
## iter 60 value 394.988684
## iter 70 value 359.139436
## iter 80 value 307.774216
## iter 90 value 278.912817
## iter 100 value 260.941975
## iter 110 value 242.976068
## iter 120 value 224.912520
## iter 130 value 210.697153
## iter 140 value 196.916289
## iter 150 value 184.704041
## iter 160 value 173.604627
## iter 170 value 166.666951
## iter 180 value 160.416798
## iter 190 value 154.357954
## iter 200 value 150.120943
## iter 210 value 144.965282
## iter 220 value 141.362726
## iter 230 value 135.811102
## iter 240 value 130.980937
## iter 250 value 128.243357
## iter 260 value 125.070794
## iter 270 value 122.509514
## iter 280 value 119.135124
## iter 290 value 112.570023
## iter 300 value 110.027261
## iter 310 value 107.064712
## iter 320 value 103.900749
## iter 330 value 102.107135
## iter 340 value 100.732817
## iter 350 value 99.894750
## iter 360 value 98.761885
## iter 370 value 98.528319
## iter 380 value 98.425918
## iter 390 value 98.223349
## iter 400 value 98.038588
## iter 410 value 97.607331
## iter 420 value 97.101352
## iter 430 value 96.323794
## iter 440 value 95.605735
## iter 450 value 95.083086
## iter 460 value 94.254628
## iter 470 value 93.481267
## iter 480 value 93.065227
## iter 490 value 92.681216
## iter 500 value 92.223155
## final value 92.223155
## stopped after 500 iterations
## # weights: 241
## initial value 1447577.885567
## iter 10 value 2744.914996
## iter 20 value 1146.500928
## iter 30 value 710.132165
## iter 40 value 535.028915
## iter 50 value 457.015547
## iter 60 value 364.493274
## iter 70 value 295.639634
## iter 80 value 246.729285
## iter 90 value 218.204345
## iter 100 value 200.386817
## iter 110 value 187.434772
## iter 120 value 179.928689
## iter 130 value 172.975121
## iter 140 value 168.659944
## iter 150 value 164.002296
## iter 160 value 160.826762
## iter 170 value 157.833650
## iter 180 value 153.927268
## iter 190 value 148.447355
## iter 200 value 141.176566
## iter 210 value 134.207882
## iter 220 value 128.623969
## iter 230 value 122.603852
## iter 240 value 117.775713
## iter 250 value 114.418955
## iter 260 value 111.123711
## iter 270 value 108.801299
## iter 280 value 106.559617
## iter 290 value 104.848651
## iter 300 value 103.161185
## iter 310 value 101.336099
## iter 320 value 99.485886
## iter 330 value 97.426213
## iter 340 value 95.503081
## iter 350 value 93.851554
## iter 360 value 92.228853
## iter 370 value 90.614183
## iter 380 value 88.174462
## iter 390 value 85.935415
## iter 400 value 84.092588
## iter 410 value 82.507959
## iter 420 value 80.844932
## iter 430 value 79.050224
## iter 440 value 77.653474
## iter 450 value 76.659325
## iter 460 value 75.789712
## iter 470 value 75.197178
## iter 480 value 74.450062
## iter 490 value 74.161486
## iter 500 value 74.124362
## final value 74.124362
## stopped after 500 iterations
## # weights: 25
## initial value 1399249.548941
## iter 10 value 7003.306263
## iter 20 value 5131.185983
## iter 30 value 2857.338188
## iter 40 value 1762.670837
## iter 50 value 1286.671748
## iter 60 value 1246.243551
## iter 70 value 1225.658074
## iter 80 value 1158.227390
## iter 90 value 1143.215915
## iter 100 value 1135.833051
## iter 110 value 1134.240530
## iter 120 value 1133.685525
## iter 130 value 1132.255449
## iter 140 value 1130.797270
## iter 150 value 1129.767567
## iter 160 value 1129.447015
## iter 170 value 1129.435259
## iter 180 value 1129.239655
## iter 190 value 1128.799157
## iter 200 value 1128.619499
## final value 1128.583892
## converged
## # weights: 61
## initial value 1370760.779359
## iter 10 value 19907.146053
## iter 20 value 3239.762629
## iter 30 value 3102.842849
## iter 40 value 3013.132086
## iter 50 value 2948.650387
## iter 60 value 2893.249232
## iter 70 value 2819.007379
## iter 80 value 2782.237329
## iter 90 value 2762.406226
## iter 100 value 2758.731294
## iter 110 value 2755.521585
## iter 120 value 2751.978833
## iter 130 value 2735.855152
## iter 140 value 2733.920928
## iter 150 value 2733.262579
## iter 160 value 2732.679618
## iter 170 value 2731.842361
## iter 180 value 2731.573955
## iter 190 value 2729.314204
## iter 200 value 2713.701059
## iter 210 value 2706.317183
## iter 220 value 2691.657400
## iter 230 value 2689.880493
## iter 240 value 2678.279249
## iter 250 value 2667.300336
## iter 260 value 2666.123031
## iter 270 value 2640.826370
## iter 280 value 2638.156135
## iter 290 value 2628.133612
## iter 300 value 2403.963658
## iter 310 value 2044.160707
## iter 320 value 1475.744751
## iter 330 value 1272.201363
## iter 340 value 1206.136339
## iter 350 value 1194.682270
## iter 360 value 1190.650892
## iter 370 value 1189.620567
## iter 380 value 1189.118859
## iter 390 value 1187.988302
## iter 400 value 1186.085736
## iter 410 value 1186.062944
## final value 1186.062177
## converged
## # weights: 121
## initial value 1351488.804482
## iter 10 value 1274.506092
## iter 20 value 900.715802
## iter 30 value 757.656657
## iter 40 value 644.489324
## iter 50 value 564.932621
## iter 60 value 520.276777
## iter 70 value 471.698120
## iter 80 value 442.779524
## iter 90 value 422.462933
## iter 100 value 408.357824
## iter 110 value 395.506349
## iter 120 value 384.665575
## iter 130 value 376.388211
## iter 140 value 368.343874
## iter 150 value 361.125107
## iter 160 value 354.937938
## iter 170 value 350.923256
## iter 180 value 344.337857
## iter 190 value 337.660117
## iter 200 value 333.167933
## iter 210 value 330.114755
## iter 220 value 327.808591
## iter 230 value 324.523861
## iter 240 value 322.006802
## iter 250 value 320.853066
## iter 260 value 320.224749
## iter 270 value 317.176238
## iter 280 value 313.440666
## iter 290 value 310.918030
## iter 300 value 309.519708
## iter 310 value 307.794084
## iter 320 value 303.841030
## iter 330 value 300.399886
## iter 340 value 299.564688
## iter 350 value 298.166221
## iter 360 value 297.639823
## iter 370 value 297.337774
## iter 380 value 295.457185
## iter 390 value 293.231000
## iter 400 value 292.067611
## iter 410 value 290.788416
## iter 420 value 287.644827
## iter 430 value 285.054687
## iter 440 value 283.389651
## iter 450 value 281.085013
## iter 460 value 278.140069
## iter 470 value 276.125443
## iter 480 value 274.818999
## iter 490 value 273.786823
## iter 500 value 273.615208
## final value 273.615208
## stopped after 500 iterations
## # weights: 181
## initial value 1415817.314266
## iter 10 value 1153.746106
## iter 20 value 832.706463
## iter 30 value 736.038801
## iter 40 value 615.890780
## iter 50 value 494.425847
## iter 60 value 430.101505
## iter 70 value 376.750882
## iter 80 value 328.624886
## iter 90 value 303.685305
## iter 100 value 278.555990
## iter 110 value 251.284476
## iter 120 value 237.195157
## iter 130 value 224.938165
## iter 140 value 215.014507
## iter 150 value 208.263521
## iter 160 value 202.777010
## iter 170 value 198.386358
## iter 180 value 193.232424
## iter 190 value 189.899769
## iter 200 value 186.888340
## iter 210 value 182.727949
## iter 220 value 180.164476
## iter 230 value 176.324788
## iter 240 value 174.429170
## iter 250 value 173.034823
## iter 260 value 172.218962
## iter 270 value 171.557560
## iter 280 value 171.038350
## iter 290 value 170.584040
## iter 300 value 170.214840
## iter 310 value 169.397091
## iter 320 value 168.188245
## iter 330 value 166.969432
## iter 340 value 165.835678
## iter 350 value 164.841096
## iter 360 value 163.915017
## iter 370 value 163.153419
## iter 380 value 162.791262
## iter 390 value 162.174939
## iter 400 value 161.237640
## iter 410 value 160.027661
## iter 420 value 158.675966
## iter 430 value 157.343618
## iter 440 value 155.833902
## iter 450 value 154.797980
## iter 460 value 153.169628
## iter 470 value 150.788225
## iter 480 value 148.332568
## iter 490 value 145.043753
## iter 500 value 142.875852
## final value 142.875852
## stopped after 500 iterations
## # weights: 241
## initial value 1369819.313009
## iter 10 value 1539.309919
## iter 20 value 838.402909
## iter 30 value 728.270365
## iter 40 value 562.696980
## iter 50 value 438.775254
## iter 60 value 372.495928
## iter 70 value 320.728187
## iter 80 value 289.415911
## iter 90 value 248.421982
## iter 100 value 220.815497
## iter 110 value 204.095880
## iter 120 value 186.211479
## iter 130 value 171.728556
## iter 140 value 160.688435
## iter 150 value 153.035179
## iter 160 value 146.617843
## iter 170 value 138.845792
## iter 180 value 133.165401
## iter 190 value 129.035622
## iter 200 value 124.474376
## iter 210 value 119.018202
## iter 220 value 112.296760
## iter 230 value 107.162626
## iter 240 value 103.136083
## iter 250 value 97.478991
## iter 260 value 92.199305
## iter 270 value 88.770772
## iter 280 value 87.430672
## iter 290 value 86.579492
## iter 300 value 86.023614
## iter 310 value 84.610445
## iter 320 value 83.296348
## iter 330 value 82.022265
## iter 340 value 80.599078
## iter 350 value 79.400415
## iter 360 value 78.445209
## iter 370 value 77.183573
## iter 380 value 75.491217
## iter 390 value 72.918922
## iter 400 value 70.337756
## iter 410 value 68.710865
## iter 420 value 66.708595
## iter 430 value 64.351618
## iter 440 value 60.124377
## iter 450 value 55.873978
## iter 460 value 53.803475
## iter 470 value 52.027364
## iter 480 value 50.823363
## iter 490 value 50.311411
## iter 500 value 50.163579
## final value 50.163579
## stopped after 500 iterations
## # weights: 25
## initial value 1400994.033681
## iter 10 value 7955.493644
## iter 20 value 4601.359282
## iter 30 value 3276.361673
## iter 40 value 2016.038266
## iter 50 value 1769.808288
## iter 60 value 1634.133437
## iter 70 value 1395.903713
## iter 80 value 1277.046361
## iter 90 value 1237.554775
## iter 100 value 1220.745836
## iter 110 value 1207.940911
## iter 120 value 1192.950222
## iter 130 value 1190.361158
## final value 1190.338875
## converged
## # weights: 61
## initial value 1421368.223731
## iter 10 value 4612.660994
## iter 20 value 2656.081762
## iter 30 value 2230.571394
## iter 40 value 1952.037648
## iter 50 value 1604.357762
## iter 60 value 1281.314455
## iter 70 value 1155.182669
## iter 80 value 1085.957883
## iter 90 value 1027.889277
## iter 100 value 975.513376
## iter 110 value 945.952700
## iter 120 value 934.648888
## iter 130 value 928.719033
## iter 140 value 924.923107
## iter 150 value 924.209844
## iter 160 value 922.794149
## iter 170 value 919.344881
## iter 180 value 912.969396
## iter 190 value 909.098227
## iter 200 value 904.750679
## iter 210 value 889.186522
## iter 220 value 880.361651
## iter 230 value 878.443738
## iter 240 value 877.397079
## iter 250 value 876.936345
## iter 260 value 874.059015
## iter 270 value 872.065119
## iter 280 value 870.797129
## iter 290 value 868.427374
## iter 300 value 862.199592
## iter 310 value 847.690135
## iter 320 value 839.174099
## iter 330 value 832.954565
## iter 340 value 830.164821
## iter 350 value 829.945493
## iter 360 value 829.915428
## iter 370 value 829.908993
## iter 370 value 829.908988
## iter 370 value 829.908988
## final value 829.908988
## converged
## # weights: 121
## initial value 1415604.581887
## iter 10 value 1227.769186
## iter 20 value 953.745401
## iter 30 value 806.372137
## iter 40 value 732.668901
## iter 50 value 682.436079
## iter 60 value 646.732870
## iter 70 value 630.909842
## iter 80 value 617.691505
## iter 90 value 599.218991
## iter 100 value 583.782858
## iter 110 value 568.961902
## iter 120 value 557.690120
## iter 130 value 549.099975
## iter 140 value 543.626355
## iter 150 value 537.202356
## iter 160 value 533.899409
## iter 170 value 526.474566
## iter 180 value 522.856691
## iter 190 value 519.471537
## iter 200 value 514.946737
## iter 210 value 511.605796
## iter 220 value 506.280424
## iter 230 value 503.509765
## iter 240 value 499.352679
## iter 250 value 496.877813
## iter 260 value 495.376233
## iter 270 value 493.164283
## iter 280 value 492.013969
## iter 290 value 490.904499
## iter 300 value 490.447125
## iter 310 value 490.142662
## iter 320 value 489.946515
## iter 330 value 489.869201
## iter 340 value 489.851547
## iter 350 value 489.849968
## final value 489.849843
## converged
## # weights: 181
## initial value 1355874.647659
## iter 10 value 1268.762649
## iter 20 value 891.091397
## iter 30 value 786.235972
## iter 40 value 681.113258
## iter 50 value 589.561179
## iter 60 value 551.597653
## iter 70 value 524.484523
## iter 80 value 501.372322
## iter 90 value 486.972949
## iter 100 value 476.462205
## iter 110 value 466.885857
## iter 120 value 457.812185
## iter 130 value 449.434337
## iter 140 value 437.278834
## iter 150 value 427.452600
## iter 160 value 417.590269
## iter 170 value 410.122513
## iter 180 value 403.411050
## iter 190 value 398.432436
## iter 200 value 392.773472
## iter 210 value 389.850201
## iter 220 value 387.486318
## iter 230 value 386.011127
## iter 240 value 384.624139
## iter 250 value 381.979273
## iter 260 value 379.263554
## iter 270 value 377.934958
## iter 280 value 376.958222
## iter 290 value 376.446093
## iter 300 value 376.054584
## iter 310 value 375.569120
## iter 320 value 375.023193
## iter 330 value 374.599039
## iter 340 value 374.019086
## iter 350 value 373.631419
## iter 360 value 373.334406
## iter 370 value 373.200845
## iter 380 value 373.078076
## iter 390 value 372.948732
## iter 400 value 372.846201
## iter 410 value 372.814350
## iter 420 value 372.794035
## iter 430 value 372.783049
## iter 440 value 372.780478
## iter 450 value 372.779891
## final value 372.779828
## converged
## # weights: 241
## initial value 1372262.491724
## iter 10 value 1261.420175
## iter 20 value 903.327745
## iter 30 value 786.358543
## iter 40 value 659.204554
## iter 50 value 578.880153
## iter 60 value 539.695219
## iter 70 value 510.418071
## iter 80 value 485.319447
## iter 90 value 462.890066
## iter 100 value 443.788352
## iter 110 value 435.121937
## iter 120 value 429.100086
## iter 130 value 421.543425
## iter 140 value 416.127981
## iter 150 value 411.128164
## iter 160 value 405.615261
## iter 170 value 400.909078
## iter 180 value 397.308013
## iter 190 value 395.236362
## iter 200 value 393.160042
## iter 210 value 390.764108
## iter 220 value 387.820626
## iter 230 value 382.183228
## iter 240 value 371.001090
## iter 250 value 362.754123
## iter 260 value 356.765498
## iter 270 value 351.158632
## iter 280 value 346.726676
## iter 290 value 343.983664
## iter 300 value 341.073193
## iter 310 value 338.323878
## iter 320 value 335.814223
## iter 330 value 333.789370
## iter 340 value 332.110082
## iter 350 value 330.837736
## iter 360 value 329.500651
## iter 370 value 328.821603
## iter 380 value 328.123725
## iter 390 value 327.284490
## iter 400 value 326.824997
## iter 410 value 326.579386
## iter 420 value 326.424029
## iter 430 value 326.262638
## iter 440 value 325.792207
## iter 450 value 324.905723
## iter 460 value 324.172357
## iter 470 value 323.327584
## iter 480 value 322.741780
## iter 490 value 322.509284
## iter 500 value 322.257719
## final value 322.257719
## stopped after 500 iterations
## # weights: 25
## initial value 1369324.548606
## iter 10 value 7029.271043
## iter 20 value 5520.054754
## iter 30 value 3642.486405
## iter 40 value 2619.572918
## iter 50 value 1567.967587
## iter 60 value 1480.984936
## iter 70 value 1461.370256
## iter 80 value 1451.908237
## iter 90 value 1426.602109
## iter 100 value 1417.057151
## iter 110 value 1411.328755
## iter 120 value 1410.344311
## iter 130 value 1409.957226
## iter 140 value 1403.711795
## iter 150 value 1370.224330
## iter 160 value 1355.550457
## iter 170 value 1349.305219
## iter 180 value 1306.933094
## iter 190 value 1288.582534
## iter 200 value 1163.049697
## iter 210 value 992.138398
## iter 220 value 935.697053
## iter 230 value 928.871012
## iter 240 value 928.707657
## iter 250 value 923.356768
## iter 260 value 917.870399
## iter 270 value 916.347507
## iter 280 value 914.823268
## iter 290 value 914.821357
## iter 300 value 914.813899
## iter 310 value 914.791238
## final value 914.790295
## converged
## # weights: 61
## initial value 1403025.240253
## iter 10 value 4728.121653
## iter 20 value 2673.376292
## iter 30 value 1989.686211
## iter 40 value 1598.316078
## iter 50 value 1142.365653
## iter 60 value 938.230331
## iter 70 value 823.159675
## iter 80 value 782.372632
## iter 90 value 751.734381
## iter 100 value 723.270320
## iter 110 value 709.153813
## iter 120 value 690.189915
## iter 130 value 682.817015
## iter 140 value 681.126544
## iter 150 value 676.342571
## iter 160 value 664.105838
## iter 170 value 657.703261
## iter 180 value 653.514365
## iter 190 value 651.402453
## iter 200 value 647.976011
## iter 210 value 646.449817
## iter 220 value 645.405267
## iter 230 value 643.520719
## iter 240 value 641.420334
## iter 250 value 639.915630
## iter 260 value 639.633704
## iter 270 value 639.039992
## iter 280 value 637.250225
## iter 290 value 634.685070
## iter 300 value 629.834822
## iter 310 value 628.067869
## iter 320 value 625.918633
## iter 330 value 620.157621
## iter 340 value 614.206570
## iter 350 value 602.877392
## iter 360 value 590.663175
## iter 370 value 584.956312
## iter 380 value 584.585613
## iter 390 value 584.085472
## iter 400 value 583.271979
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## final value 578.617326
## stopped after 500 iterations
## # weights: 121
## initial value 1320129.751655
## iter 10 value 1625.228776
## iter 20 value 915.604214
## iter 30 value 790.285464
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## final value 270.219184
## stopped after 500 iterations
## # weights: 181
## initial value 1397857.787408
## iter 10 value 1390.762477
## iter 20 value 826.968495
## iter 30 value 667.515999
## iter 40 value 541.885557
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## final value 158.141032
## stopped after 500 iterations
## # weights: 241
## initial value 1359008.804843
## iter 10 value 1463.672232
## iter 20 value 724.554487
## iter 30 value 567.537212
## iter 40 value 444.004569
## iter 50 value 359.217529
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## iter 490 value 32.927526
## iter 500 value 32.901611
## final value 32.901611
## stopped after 500 iterations
## # weights: 25
## initial value 1417650.248740
## iter 10 value 4079.225355
## iter 20 value 2720.183206
## iter 30 value 2153.001104
## iter 40 value 1266.636488
## iter 50 value 1009.920115
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## iter 250 value 912.961737
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## iter 320 value 912.366370
## iter 330 value 912.335954
## iter 340 value 912.196131
## iter 350 value 912.186757
## final value 912.186740
## converged
## # weights: 61
## initial value 1401538.991714
## iter 10 value 3097.422086
## iter 20 value 1321.082665
## iter 30 value 1084.749567
## iter 40 value 928.812640
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## iter 240 value 594.636335
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## iter 300 value 593.742283
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## iter 340 value 591.251286
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## iter 480 value 579.709386
## iter 490 value 579.176393
## iter 500 value 579.061910
## final value 579.061910
## stopped after 500 iterations
## # weights: 121
## initial value 1387618.117100
## iter 10 value 2753.266913
## iter 20 value 1898.073403
## iter 30 value 1368.692358
## iter 40 value 1060.779871
## iter 50 value 867.633050
## iter 60 value 761.397845
## iter 70 value 663.727604
## iter 80 value 606.383719
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## iter 500 value 325.464283
## final value 325.464283
## stopped after 500 iterations
## # weights: 181
## initial value 1353109.831716
## iter 10 value 1164.641755
## iter 20 value 842.889898
## iter 30 value 654.110766
## iter 40 value 535.081291
## iter 50 value 457.116836
## iter 60 value 400.020065
## iter 70 value 347.795060
## iter 80 value 308.961724
## iter 90 value 284.603092
## iter 100 value 266.385862
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## iter 480 value 125.151554
## iter 490 value 124.254815
## iter 500 value 123.839458
## final value 123.839458
## stopped after 500 iterations
## # weights: 241
## initial value 1405068.188133
## iter 10 value 1595.043138
## iter 20 value 794.538815
## iter 30 value 622.075932
## iter 40 value 508.212107
## iter 50 value 434.460884
## iter 60 value 381.855581
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## iter 80 value 309.685292
## iter 90 value 282.875985
## iter 100 value 250.235515
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## iter 180 value 115.175385
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## iter 470 value 57.739341
## iter 480 value 57.574134
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## iter 500 value 57.491683
## final value 57.491683
## stopped after 500 iterations
## # weights: 25
## initial value 1400163.771020
## iter 10 value 5713.801711
## iter 20 value 5428.728029
## iter 30 value 4766.190000
## iter 40 value 4279.220641
## iter 50 value 3418.831557
## iter 60 value 1868.851241
## iter 70 value 1138.388766
## iter 80 value 973.124748
## iter 90 value 958.627804
## iter 100 value 945.266446
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## iter 120 value 931.612337
## iter 130 value 930.479714
## iter 140 value 929.992672
## iter 150 value 928.878328
## iter 160 value 927.360784
## iter 170 value 926.832065
## iter 180 value 925.766405
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## iter 200 value 925.710542
## iter 210 value 925.253097
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## iter 280 value 924.118729
## iter 290 value 924.116347
## iter 290 value 924.116345
## iter 300 value 924.116080
## iter 300 value 924.116079
## final value 924.115967
## converged
## # weights: 61
## initial value 1385279.120146
## iter 10 value 2381.166495
## iter 20 value 1422.384054
## iter 30 value 1194.124622
## iter 40 value 1024.783772
## iter 50 value 913.654068
## iter 60 value 834.222527
## iter 70 value 778.881039
## iter 80 value 717.896567
## iter 90 value 678.846658
## iter 100 value 661.933289
## iter 110 value 646.737066
## iter 120 value 640.994490
## iter 130 value 638.638933
## iter 140 value 637.807904
## iter 150 value 637.174873
## iter 160 value 636.854474
## iter 170 value 635.755854
## iter 180 value 634.507177
## iter 190 value 633.811073
## iter 200 value 631.931574
## iter 210 value 627.945953
## iter 220 value 622.966155
## iter 230 value 619.847091
## iter 240 value 618.189197
## iter 250 value 617.795579
## iter 260 value 617.536832
## iter 270 value 617.505557
## iter 280 value 617.448387
## iter 290 value 617.297451
## iter 300 value 617.062309
## iter 310 value 616.858225
## iter 320 value 616.746581
## iter 330 value 615.559596
## iter 340 value 614.895280
## iter 350 value 613.692400
## iter 360 value 613.231901
## iter 370 value 613.101282
## iter 380 value 613.032528
## iter 390 value 612.895499
## iter 400 value 612.781296
## iter 410 value 612.717414
## iter 420 value 612.524121
## iter 430 value 612.344326
## iter 440 value 612.329520
## iter 450 value 612.321211
## final value 612.321145
## converged
## # weights: 121
## initial value 1417212.408072
## iter 10 value 5280.796476
## iter 20 value 1688.081874
## iter 30 value 1234.127556
## iter 40 value 1007.940097
## iter 50 value 837.389238
## iter 60 value 754.129475
## iter 70 value 734.818821
## iter 80 value 724.310592
## iter 90 value 694.662279
## iter 100 value 664.287987
## iter 110 value 647.795237
## iter 120 value 628.397121
## iter 130 value 618.382490
## iter 140 value 601.778486
## iter 150 value 588.765063
## iter 160 value 579.641514
## iter 170 value 572.482270
## iter 180 value 566.658088
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## iter 200 value 558.693466
## iter 210 value 551.187800
## iter 220 value 543.161134
## iter 230 value 537.673573
## iter 240 value 533.990321
## iter 250 value 529.678997
## iter 260 value 527.575958
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## iter 280 value 526.778131
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## iter 300 value 526.602259
## iter 310 value 526.313449
## iter 320 value 526.145045
## iter 330 value 525.997876
## iter 340 value 525.811418
## iter 350 value 525.798071
## iter 360 value 525.791518
## iter 370 value 525.778542
## iter 380 value 525.651566
## iter 390 value 522.813974
## iter 400 value 517.316646
## iter 410 value 516.673200
## iter 420 value 516.326686
## iter 430 value 516.172212
## iter 440 value 516.101253
## iter 450 value 516.040796
## iter 460 value 515.868078
## iter 470 value 515.784470
## iter 480 value 514.908834
## iter 490 value 514.074078
## iter 500 value 513.029929
## final value 513.029929
## stopped after 500 iterations
## # weights: 181
## initial value 1389328.202115
## iter 10 value 1244.986765
## iter 20 value 841.651698
## iter 30 value 694.103831
## iter 40 value 539.807267
## iter 50 value 470.187909
## iter 60 value 437.291794
## iter 70 value 409.322621
## iter 80 value 359.162507
## iter 90 value 305.667595
## iter 100 value 272.434827
## iter 110 value 245.040035
## iter 120 value 226.838346
## iter 130 value 214.270839
## iter 140 value 202.473113
## iter 150 value 193.078213
## iter 160 value 187.944295
## iter 170 value 180.063615
## iter 180 value 172.096401
## iter 190 value 164.002801
## iter 200 value 152.796253
## iter 210 value 146.503368
## iter 220 value 142.046155
## iter 230 value 137.918023
## iter 240 value 129.807876
## iter 250 value 124.166018
## iter 260 value 121.593737
## iter 270 value 117.778357
## iter 280 value 116.175051
## iter 290 value 114.061840
## iter 300 value 110.921231
## iter 310 value 108.160438
## iter 320 value 105.976961
## iter 330 value 104.270152
## iter 340 value 103.047679
## iter 350 value 102.405006
## iter 360 value 101.453776
## iter 370 value 101.113202
## iter 380 value 100.903903
## iter 390 value 100.422309
## iter 400 value 99.945555
## iter 410 value 98.730365
## iter 420 value 97.335009
## iter 430 value 96.067677
## iter 440 value 95.140918
## iter 450 value 94.623030
## iter 460 value 93.966223
## iter 470 value 93.129303
## iter 480 value 92.251014
## iter 490 value 91.061222
## iter 500 value 89.599591
## final value 89.599591
## stopped after 500 iterations
## # weights: 241
## initial value 1404151.633095
## iter 10 value 1215.141017
## iter 20 value 758.976340
## iter 30 value 636.059599
## iter 40 value 539.501681
## iter 50 value 404.964221
## iter 60 value 329.502812
## iter 70 value 282.179854
## iter 80 value 251.846050
## iter 90 value 217.843289
## iter 100 value 190.685503
## iter 110 value 159.314775
## iter 120 value 140.570505
## iter 130 value 129.907216
## iter 140 value 120.152925
## iter 150 value 108.885045
## iter 160 value 100.710033
## iter 170 value 93.269918
## iter 180 value 86.415963
## iter 190 value 80.429147
## iter 200 value 75.051106
## iter 210 value 69.627941
## iter 220 value 64.623830
## iter 230 value 61.335661
## iter 240 value 57.872329
## iter 250 value 54.520209
## iter 260 value 51.124032
## iter 270 value 48.729867
## iter 280 value 47.036047
## iter 290 value 45.766156
## iter 300 value 44.547663
## iter 310 value 42.450448
## iter 320 value 40.365933
## iter 330 value 39.055769
## iter 340 value 37.891417
## iter 350 value 36.772243
## iter 360 value 35.741404
## iter 370 value 34.446509
## iter 380 value 33.192121
## iter 390 value 31.808629
## iter 400 value 30.780333
## iter 410 value 30.060431
## iter 420 value 29.603948
## iter 430 value 29.110078
## iter 440 value 28.337075
## iter 450 value 27.858182
## iter 460 value 27.598151
## iter 470 value 27.283517
## iter 480 value 26.601091
## iter 490 value 26.453481
## iter 500 value 26.416410
## final value 26.416410
## stopped after 500 iterations
## # weights: 25
## initial value 1416989.041823
## iter 10 value 2960.777171
## iter 20 value 1719.661239
## iter 30 value 1410.996786
## iter 40 value 1249.601392
## iter 50 value 1185.450602
## iter 60 value 1174.047284
## iter 70 value 1160.482656
## iter 80 value 1133.990520
## iter 90 value 1042.532070
## iter 100 value 963.746576
## iter 110 value 946.125407
## iter 120 value 939.834232
## iter 130 value 916.539892
## iter 140 value 916.169692
## iter 150 value 915.643496
## iter 160 value 915.563343
## iter 170 value 915.561790
## iter 180 value 915.548558
## iter 190 value 915.479505
## iter 200 value 915.465998
## final value 915.465940
## converged
## # weights: 61
## initial value 1376621.149496
## iter 10 value 16065.394452
## iter 20 value 13781.148611
## iter 30 value 7675.871127
## iter 40 value 4916.573484
## iter 50 value 4012.861697
## iter 60 value 3534.644192
## iter 70 value 3392.922666
## iter 80 value 3372.988033
## iter 90 value 3281.374845
## iter 100 value 2928.796444
## iter 110 value 2719.223246
## iter 120 value 2589.669803
## iter 130 value 2443.890697
## iter 140 value 2111.945079
## iter 150 value 1636.519692
## iter 160 value 1445.032852
## iter 170 value 1338.128519
## iter 180 value 1320.020574
## iter 190 value 1291.274325
## iter 200 value 1232.413561
## iter 210 value 1029.095621
## iter 220 value 971.659463
## iter 230 value 950.468630
## iter 240 value 933.611948
## iter 250 value 914.779687
## iter 260 value 900.079159
## iter 270 value 892.927943
## iter 280 value 888.280447
## iter 290 value 885.154634
## iter 300 value 884.374327
## iter 310 value 883.577239
## iter 320 value 883.563140
## iter 330 value 883.461320
## iter 340 value 882.870279
## iter 350 value 882.840623
## iter 360 value 882.699917
## iter 370 value 881.846974
## iter 380 value 881.669647
## iter 390 value 878.787846
## iter 400 value 877.665008
## iter 410 value 876.869440
## iter 420 value 876.093261
## iter 430 value 875.652578
## iter 440 value 875.408594
## iter 450 value 875.267781
## iter 460 value 874.215600
## iter 470 value 874.067983
## iter 480 value 873.997632
## iter 490 value 873.828343
## iter 500 value 873.827342
## final value 873.827342
## stopped after 500 iterations
## # weights: 121
## initial value 1447298.929750
## iter 10 value 5181.929340
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## iter 40 value 1004.525538
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## iter 490 value 413.482492
## iter 500 value 413.303608
## final value 413.303608
## stopped after 500 iterations
## # weights: 181
## initial value 1401242.562861
## iter 10 value 1038.030963
## iter 20 value 806.540386
## iter 30 value 653.029115
## iter 40 value 528.121089
## iter 50 value 430.921571
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## iter 300 value 93.706192
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## iter 320 value 91.528801
## iter 330 value 90.743983
## iter 340 value 89.936034
## iter 350 value 89.118237
## iter 360 value 88.710885
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## iter 460 value 83.814283
## iter 470 value 83.222364
## iter 480 value 82.792683
## iter 490 value 82.264200
## iter 500 value 81.437220
## final value 81.437220
## stopped after 500 iterations
## # weights: 241
## initial value 1383509.087273
## iter 10 value 1553.884628
## iter 20 value 808.961596
## iter 30 value 616.847233
## iter 40 value 519.128414
## iter 50 value 429.490467
## iter 60 value 361.784154
## iter 70 value 311.438305
## iter 80 value 275.671889
## iter 90 value 241.221776
## iter 100 value 206.198722
## iter 110 value 180.662010
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## iter 150 value 121.066071
## iter 160 value 115.024752
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## iter 190 value 98.988901
## iter 200 value 94.028299
## iter 210 value 87.650714
## iter 220 value 83.727167
## iter 230 value 79.972966
## iter 240 value 76.312975
## iter 250 value 73.292122
## iter 260 value 70.790747
## iter 270 value 68.651743
## iter 280 value 66.599493
## iter 290 value 64.516045
## iter 300 value 61.447797
## iter 310 value 58.357381
## iter 320 value 56.272259
## iter 330 value 54.063302
## iter 340 value 51.591521
## iter 350 value 49.956701
## iter 360 value 47.533085
## iter 370 value 44.453532
## iter 380 value 42.270522
## iter 390 value 40.604199
## iter 400 value 39.091190
## iter 410 value 37.786348
## iter 420 value 36.738922
## iter 430 value 35.781893
## iter 440 value 34.683175
## iter 450 value 33.327122
## iter 460 value 32.091603
## iter 470 value 30.910393
## iter 480 value 29.783656
## iter 490 value 29.194707
## iter 500 value 28.986760
## final value 28.986760
## stopped after 500 iterations
## # weights: 25
## initial value 1402566.442260
## iter 10 value 6726.057403
## iter 20 value 5552.715900
## iter 30 value 4708.597370
## iter 40 value 2770.514225
## iter 50 value 1686.500964
## iter 60 value 1470.549296
## iter 70 value 1372.380283
## iter 80 value 1351.850000
## iter 90 value 1306.243563
## iter 100 value 1231.538963
## iter 110 value 1203.274197
## iter 120 value 1201.192130
## iter 130 value 1200.798648
## iter 140 value 1199.616964
## final value 1199.434426
## converged
## # weights: 61
## initial value 1394755.557612
## iter 10 value 8782.134814
## iter 20 value 8243.082055
## iter 30 value 6559.537649
## iter 40 value 4298.018993
## iter 50 value 2835.537276
## iter 60 value 2146.314991
## iter 70 value 1765.966002
## iter 80 value 1601.639379
## iter 90 value 1422.313760
## iter 100 value 1269.018337
## iter 110 value 1192.177176
## iter 120 value 1144.823838
## iter 130 value 1102.857989
## iter 140 value 1057.570147
## iter 150 value 1008.799150
## iter 160 value 975.248580
## iter 170 value 968.210946
## iter 180 value 954.774144
## iter 190 value 937.718569
## iter 200 value 915.595762
## iter 210 value 898.296674
## iter 220 value 885.778288
## iter 230 value 869.773952
## iter 240 value 866.164853
## iter 250 value 861.960647
## iter 260 value 858.232510
## iter 270 value 857.375740
## iter 280 value 856.974669
## iter 290 value 856.915007
## iter 300 value 856.889117
## iter 310 value 856.871027
## iter 320 value 856.857169
## iter 330 value 856.850935
## final value 856.850046
## converged
## # weights: 121
## initial value 1367477.747105
## iter 10 value 1768.419131
## iter 20 value 1203.581706
## iter 30 value 931.875918
## iter 40 value 825.073254
## iter 50 value 770.759876
## iter 60 value 709.900773
## iter 70 value 669.606119
## iter 80 value 642.202656
## iter 90 value 627.880234
## iter 100 value 614.746524
## iter 110 value 599.277602
## iter 120 value 588.394684
## iter 130 value 583.114386
## iter 140 value 578.624098
## iter 150 value 572.087223
## iter 160 value 560.149334
## iter 170 value 552.073114
## iter 180 value 540.965897
## iter 190 value 535.546346
## iter 200 value 532.272992
## iter 210 value 529.138767
## iter 220 value 526.869453
## iter 230 value 525.768212
## iter 240 value 524.709287
## iter 250 value 524.140692
## iter 260 value 523.876788
## iter 270 value 523.183766
## iter 280 value 522.772033
## iter 290 value 522.398290
## iter 300 value 522.188404
## iter 310 value 522.088319
## iter 320 value 522.057748
## iter 330 value 522.053319
## iter 340 value 522.052875
## final value 522.052853
## converged
## # weights: 181
## initial value 1417207.166820
## iter 10 value 1235.252851
## iter 20 value 917.488588
## iter 30 value 799.594353
## iter 40 value 715.246819
## iter 50 value 615.962775
## iter 60 value 547.874749
## iter 70 value 522.468032
## iter 80 value 499.184157
## iter 90 value 472.208772
## iter 100 value 454.838902
## iter 110 value 447.235990
## iter 120 value 441.005401
## iter 130 value 435.684474
## iter 140 value 426.121210
## iter 150 value 419.383170
## iter 160 value 413.593081
## iter 170 value 408.060037
## iter 180 value 404.569289
## iter 190 value 398.981785
## iter 200 value 393.913025
## iter 210 value 389.488416
## iter 220 value 383.621049
## iter 230 value 379.795020
## iter 240 value 376.560243
## iter 250 value 373.854890
## iter 260 value 370.938757
## iter 270 value 369.386121
## iter 280 value 367.764458
## iter 290 value 366.564922
## iter 300 value 365.856319
## iter 310 value 365.301535
## iter 320 value 364.833518
## iter 330 value 364.405970
## iter 340 value 364.214612
## iter 350 value 364.049594
## iter 360 value 363.953957
## iter 370 value 363.911548
## iter 380 value 363.860473
## iter 390 value 363.771026
## iter 400 value 363.669217
## iter 410 value 363.630900
## iter 420 value 363.612402
## iter 430 value 363.601128
## iter 440 value 363.579392
## iter 450 value 363.544510
## iter 460 value 363.488872
## iter 470 value 363.409988
## iter 480 value 363.323867
## iter 490 value 363.268130
## iter 500 value 363.136861
## final value 363.136861
## stopped after 500 iterations
## # weights: 241
## initial value 1393326.487461
## iter 10 value 1313.639497
## iter 20 value 962.409426
## iter 30 value 794.591473
## iter 40 value 683.057396
## iter 50 value 583.167900
## iter 60 value 530.227590
## iter 70 value 505.074253
## iter 80 value 493.361203
## iter 90 value 471.146325
## iter 100 value 448.178492
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## iter 120 value 419.784483
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## iter 150 value 401.964208
## iter 160 value 398.719022
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## iter 180 value 392.278610
## iter 190 value 388.256973
## iter 200 value 384.128400
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## iter 220 value 379.498014
## iter 230 value 376.607533
## iter 240 value 373.676958
## iter 250 value 369.405717
## iter 260 value 365.629887
## iter 270 value 363.285642
## iter 280 value 361.708804
## iter 290 value 359.031965
## iter 300 value 356.438822
## iter 310 value 354.415395
## iter 320 value 351.676388
## iter 330 value 348.446662
## iter 340 value 346.435856
## iter 350 value 344.319565
## iter 360 value 343.303791
## iter 370 value 342.487341
## iter 380 value 341.799617
## iter 390 value 341.248887
## iter 400 value 340.657747
## iter 410 value 340.277499
## iter 420 value 340.029442
## iter 430 value 339.886577
## iter 440 value 339.738245
## iter 450 value 339.585764
## iter 460 value 339.383795
## iter 470 value 339.214297
## iter 480 value 338.835534
## iter 490 value 338.443343
## iter 500 value 338.019234
## final value 338.019234
## stopped after 500 iterations
## # weights: 25
## initial value 1431258.854456
## iter 10 value 6446.527863
## iter 20 value 5131.989858
## iter 30 value 2210.026693
## iter 40 value 1332.912023
## iter 50 value 1141.008505
## iter 60 value 1134.657501
## iter 70 value 1127.787548
## iter 80 value 1092.072780
## iter 90 value 1076.092078
## iter 100 value 1066.317952
## iter 110 value 1062.935047
## iter 120 value 1061.219973
## iter 130 value 1052.143180
## iter 140 value 1042.354242
## iter 150 value 1035.814338
## iter 160 value 1030.676737
## iter 170 value 1029.728286
## iter 180 value 1021.022758
## iter 190 value 1004.381999
## iter 200 value 998.228276
## iter 210 value 997.006664
## iter 220 value 996.755801
## iter 230 value 996.272145
## iter 240 value 995.895654
## final value 995.880378
## converged
## # weights: 61
## initial value 1362357.540075
## iter 10 value 14388.108612
## iter 20 value 3501.711018
## iter 30 value 2363.539527
## iter 40 value 2008.279237
## iter 50 value 1917.222002
## iter 60 value 1898.000094
## iter 70 value 1885.481407
## iter 80 value 1863.525315
## iter 90 value 1832.443887
## iter 100 value 1823.424649
## iter 110 value 1810.300013
## iter 120 value 1801.385356
## iter 130 value 1791.231213
## iter 140 value 1770.002519
## iter 150 value 1723.330424
## iter 160 value 1704.583613
## iter 170 value 1613.491639
## iter 180 value 1476.279016
## iter 190 value 1315.588124
## iter 200 value 1237.454027
## iter 210 value 1168.856329
## iter 220 value 1085.479279
## iter 230 value 896.533056
## iter 240 value 852.457279
## iter 250 value 843.841810
## iter 260 value 836.731731
## iter 270 value 832.622915
## iter 280 value 829.875713
## iter 290 value 826.180427
## iter 300 value 824.921245
## iter 310 value 824.708015
## iter 320 value 824.617284
## iter 330 value 824.566559
## iter 340 value 824.557856
## iter 350 value 824.554891
## final value 824.554855
## converged
## # weights: 121
## initial value 1400052.966310
## iter 10 value 1358.152484
## iter 20 value 956.669897
## iter 30 value 786.586239
## iter 40 value 669.661695
## iter 50 value 624.628483
## iter 60 value 562.752924
## iter 70 value 520.002211
## iter 80 value 484.749751
## iter 90 value 454.630069
## iter 100 value 437.432488
## iter 110 value 424.946836
## iter 120 value 411.982118
## iter 130 value 390.184380
## iter 140 value 356.703759
## iter 150 value 340.874754
## iter 160 value 327.440394
## iter 170 value 320.388599
## iter 180 value 315.438669
## iter 190 value 310.148328
## iter 200 value 306.089038
## iter 210 value 302.118872
## iter 220 value 299.890431
## iter 230 value 298.899981
## iter 240 value 297.816805
## iter 250 value 297.517901
## iter 260 value 297.394638
## iter 270 value 297.133653
## iter 280 value 296.595771
## iter 290 value 296.218396
## iter 300 value 295.530583
## iter 310 value 295.095074
## iter 320 value 294.553899
## iter 330 value 294.268125
## iter 340 value 293.420659
## iter 350 value 292.639899
## iter 360 value 292.229661
## iter 370 value 292.067593
## iter 380 value 291.942772
## iter 390 value 291.919576
## iter 400 value 291.864885
## iter 410 value 291.694832
## iter 420 value 291.354773
## iter 430 value 290.790256
## iter 440 value 289.988434
## iter 450 value 286.438100
## iter 460 value 282.340856
## iter 470 value 279.829645
## iter 480 value 275.825917
## iter 490 value 273.681374
## iter 500 value 270.043666
## final value 270.043666
## stopped after 500 iterations
## # weights: 181
## initial value 1397145.173967
## iter 10 value 1107.342303
## iter 20 value 802.300054
## iter 30 value 622.039376
## iter 40 value 505.018136
## iter 50 value 383.322579
## iter 60 value 321.872358
## iter 70 value 292.057257
## iter 80 value 264.664662
## iter 90 value 244.878589
## iter 100 value 227.397092
## iter 110 value 210.518652
## iter 120 value 199.283136
## iter 130 value 191.909072
## iter 140 value 186.580358
## iter 150 value 180.252277
## iter 160 value 173.348722
## iter 170 value 167.986013
## iter 180 value 163.550617
## iter 190 value 160.015087
## iter 200 value 157.194610
## iter 210 value 154.356460
## iter 220 value 151.091332
## iter 230 value 147.863574
## iter 240 value 144.117418
## iter 250 value 141.648148
## iter 260 value 139.229020
## iter 270 value 136.914397
## iter 280 value 134.253191
## iter 290 value 131.435408
## iter 300 value 127.388204
## iter 310 value 123.358414
## iter 320 value 121.079948
## iter 330 value 118.895285
## iter 340 value 117.190109
## iter 350 value 115.735863
## iter 360 value 113.921012
## iter 370 value 112.398862
## iter 380 value 111.998585
## iter 390 value 111.362612
## iter 400 value 110.281182
## iter 410 value 109.077356
## iter 420 value 108.037110
## iter 430 value 106.672610
## iter 440 value 105.530149
## iter 450 value 104.553714
## iter 460 value 103.972618
## iter 470 value 103.332652
## iter 480 value 102.070542
## iter 490 value 100.977154
## iter 500 value 99.897462
## final value 99.897462
## stopped after 500 iterations
## # weights: 241
## initial value 1458997.687732
## iter 10 value 1728.909875
## iter 20 value 886.809325
## iter 30 value 639.532400
## iter 40 value 459.939493
## iter 50 value 369.594574
## iter 60 value 320.892189
## iter 70 value 276.442183
## iter 80 value 226.768651
## iter 90 value 191.459109
## iter 100 value 166.762053
## iter 110 value 143.849439
## iter 120 value 125.852838
## iter 130 value 113.186592
## iter 140 value 103.053678
## iter 150 value 93.675131
## iter 160 value 86.154930
## iter 170 value 81.545408
## iter 180 value 76.731857
## iter 190 value 71.212244
## iter 200 value 64.687899
## iter 210 value 60.875334
## iter 220 value 57.906336
## iter 230 value 55.642340
## iter 240 value 53.760389
## iter 250 value 51.773653
## iter 260 value 50.147536
## iter 270 value 48.197742
## iter 280 value 46.619925
## iter 290 value 45.380291
## iter 300 value 44.538232
## iter 310 value 43.750938
## iter 320 value 42.976834
## iter 330 value 42.371725
## iter 340 value 41.697246
## iter 350 value 41.001152
## iter 360 value 40.451780
## iter 370 value 39.907713
## iter 380 value 39.408045
## iter 390 value 38.990155
## iter 400 value 38.683983
## iter 410 value 38.117336
## iter 420 value 37.680795
## iter 430 value 37.319155
## iter 440 value 36.919405
## iter 450 value 36.486322
## iter 460 value 36.049742
## iter 470 value 35.597777
## iter 480 value 35.314366
## iter 490 value 35.188518
## iter 500 value 35.136736
## final value 35.136736
## stopped after 500 iterations
## # weights: 25
## initial value 1383411.363470
## final value 16085.869356
## converged
## # weights: 61
## initial value 1409907.358667
## iter 10 value 5757.509573
## iter 20 value 2958.999097
## iter 30 value 1862.493172
## iter 40 value 1505.499224
## iter 50 value 1098.572014
## iter 60 value 929.665402
## iter 70 value 880.389936
## iter 80 value 864.287927
## iter 90 value 855.777558
## iter 100 value 848.438593
## iter 110 value 844.104589
## iter 120 value 838.711053
## iter 130 value 830.271948
## iter 140 value 828.759734
## iter 150 value 827.957352
## iter 160 value 825.366560
## iter 170 value 822.013926
## iter 180 value 818.370599
## iter 190 value 816.937676
## iter 200 value 815.836712
## iter 210 value 815.297366
## iter 220 value 814.956971
## iter 230 value 811.241229
## iter 240 value 808.534521
## iter 250 value 807.247274
## iter 260 value 806.748632
## iter 270 value 806.371011
## iter 280 value 806.039333
## iter 290 value 805.762902
## iter 300 value 805.559719
## iter 310 value 805.367140
## iter 320 value 805.333624
## final value 805.332058
## converged
## # weights: 121
## initial value 1405209.960984
## iter 10 value 1509.899575
## iter 20 value 949.955615
## iter 30 value 808.573193
## iter 40 value 686.731264
## iter 50 value 624.805810
## iter 60 value 584.892478
## iter 70 value 532.726886
## iter 80 value 503.125340
## iter 90 value 474.956861
## iter 100 value 437.110137
## iter 110 value 401.536412
## iter 120 value 378.663434
## iter 130 value 361.466298
## iter 140 value 344.630927
## iter 150 value 333.807833
## iter 160 value 319.348199
## iter 170 value 312.944424
## iter 180 value 306.623299
## iter 190 value 299.706138
## iter 200 value 293.699209
## iter 210 value 289.360957
## iter 220 value 287.535018
## iter 230 value 284.388175
## iter 240 value 281.957685
## iter 250 value 280.923003
## iter 260 value 280.565345
## iter 270 value 279.975246
## iter 280 value 279.413753
## iter 290 value 278.674897
## iter 300 value 277.928405
## iter 310 value 277.288360
## iter 320 value 276.534463
## iter 330 value 275.881961
## iter 340 value 275.586819
## iter 350 value 273.936438
## iter 360 value 273.064070
## iter 370 value 272.445777
## iter 380 value 268.725253
## iter 390 value 267.266494
## iter 400 value 265.396027
## iter 410 value 263.608715
## iter 420 value 262.677919
## iter 430 value 261.153969
## iter 440 value 260.160532
## iter 450 value 259.182200
## iter 460 value 258.017731
## iter 470 value 256.090351
## iter 480 value 255.403429
## iter 490 value 255.217935
## iter 500 value 255.211877
## final value 255.211877
## stopped after 500 iterations
## # weights: 181
## initial value 1375284.441241
## iter 10 value 1210.986870
## iter 20 value 837.008587
## iter 30 value 698.629485
## iter 40 value 563.678871
## iter 50 value 453.096069
## iter 60 value 405.928706
## iter 70 value 367.335606
## iter 80 value 328.838616
## iter 90 value 299.945854
## iter 100 value 278.816411
## iter 110 value 255.566263
## iter 120 value 238.708701
## iter 130 value 227.219745
## iter 140 value 215.004770
## iter 150 value 203.260525
## iter 160 value 192.900633
## iter 170 value 184.087919
## iter 180 value 179.166004
## iter 190 value 173.628221
## iter 200 value 170.043679
## iter 210 value 167.653950
## iter 220 value 165.437132
## iter 230 value 162.647261
## iter 240 value 160.793512
## iter 250 value 157.496802
## iter 260 value 156.258937
## iter 270 value 154.741575
## iter 280 value 152.781468
## iter 290 value 150.458933
## iter 300 value 146.926095
## iter 310 value 143.943925
## iter 320 value 141.020713
## iter 330 value 139.172160
## iter 340 value 137.400723
## iter 350 value 135.664353
## iter 360 value 134.235813
## iter 370 value 133.482446
## iter 380 value 133.084559
## iter 390 value 132.576995
## iter 400 value 131.794929
## iter 410 value 130.584196
## iter 420 value 128.695810
## iter 430 value 126.435347
## iter 440 value 125.043602
## iter 450 value 124.030827
## iter 460 value 122.928770
## iter 470 value 121.847009
## iter 480 value 120.826781
## iter 490 value 119.777479
## iter 500 value 118.806467
## final value 118.806467
## stopped after 500 iterations
## # weights: 241
## initial value 1406530.155692
## iter 10 value 1439.726198
## iter 20 value 791.240007
## iter 30 value 598.083621
## iter 40 value 498.740921
## iter 50 value 421.953437
## iter 60 value 345.241243
## iter 70 value 303.421895
## iter 80 value 266.099143
## iter 90 value 235.536732
## iter 100 value 208.643190
## iter 110 value 188.380439
## iter 120 value 172.546334
## iter 130 value 159.908528
## iter 140 value 147.661096
## iter 150 value 133.624935
## iter 160 value 119.695338
## iter 170 value 106.861552
## iter 180 value 98.483211
## iter 190 value 92.215894
## iter 200 value 85.938517
## iter 210 value 80.542726
## iter 220 value 73.906730
## iter 230 value 67.683345
## iter 240 value 61.836031
## iter 250 value 55.530542
## iter 260 value 50.399867
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## iter 490 value 21.882705
## iter 500 value 21.790990
## final value 21.790990
## stopped after 500 iterations
## # weights: 25
## initial value 1424522.319439
## iter 10 value 4265.589684
## iter 20 value 2397.472156
## iter 30 value 2129.302172
## iter 40 value 1608.055140
## iter 50 value 1468.965959
## iter 60 value 1404.871251
## iter 70 value 1218.635798
## iter 80 value 1109.838412
## iter 90 value 1049.670285
## iter 100 value 971.956837
## iter 110 value 960.584457
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## iter 130 value 927.482914
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## iter 200 value 916.240658
## iter 210 value 916.223135
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## iter 230 value 915.938819
## iter 240 value 915.616802
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## iter 300 value 914.614932
## iter 310 value 914.549059
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## iter 350 value 914.486810
## iter 360 value 914.333379
## iter 370 value 914.326333
## iter 370 value 914.326329
## final value 914.326197
## converged
## # weights: 61
## initial value 1388300.358808
## iter 10 value 5033.280956
## iter 20 value 2107.840865
## iter 30 value 1510.666890
## iter 40 value 1308.792492
## iter 50 value 1167.764175
## iter 60 value 1055.677025
## iter 70 value 939.585423
## iter 80 value 829.121075
## iter 90 value 761.371244
## iter 100 value 740.853963
## iter 110 value 724.138386
## iter 120 value 698.322843
## iter 130 value 683.876771
## iter 140 value 676.076380
## iter 150 value 669.963127
## iter 160 value 662.146896
## iter 170 value 657.151312
## iter 180 value 649.835304
## iter 190 value 643.244721
## iter 200 value 639.811689
## iter 210 value 637.573599
## iter 220 value 636.185074
## iter 230 value 634.782671
## iter 240 value 631.726444
## iter 250 value 629.611682
## iter 260 value 629.488176
## iter 270 value 629.154273
## iter 280 value 628.560394
## iter 290 value 628.171302
## iter 300 value 628.019045
## iter 310 value 627.847650
## iter 320 value 627.821612
## iter 330 value 627.804271
## iter 340 value 627.761719
## iter 350 value 627.691731
## iter 360 value 627.584353
## iter 370 value 627.454028
## iter 380 value 627.452650
## iter 390 value 627.451976
## iter 400 value 627.451259
## iter 410 value 627.450196
## iter 420 value 627.440710
## iter 430 value 627.422308
## iter 440 value 627.420469
## iter 450 value 627.399582
## iter 460 value 627.398195
## iter 460 value 627.398190
## iter 460 value 627.398190
## final value 627.398190
## converged
## # weights: 121
## initial value 1387699.685308
## iter 10 value 4442.084922
## iter 20 value 1701.262215
## iter 30 value 1099.327612
## iter 40 value 890.022221
## iter 50 value 826.340952
## iter 60 value 762.613543
## iter 70 value 740.161750
## iter 80 value 726.367719
## iter 90 value 722.096916
## iter 100 value 715.322975
## iter 110 value 712.352899
## iter 120 value 709.750440
## iter 130 value 709.358158
## iter 140 value 706.982066
## iter 150 value 702.152951
## iter 160 value 697.395928
## iter 170 value 688.453447
## iter 180 value 681.988238
## iter 190 value 676.272427
## iter 200 value 658.810014
## iter 210 value 645.696516
## iter 220 value 642.904186
## iter 230 value 640.408646
## iter 240 value 637.973011
## iter 250 value 635.218009
## iter 260 value 634.917775
## iter 270 value 633.407233
## iter 280 value 631.186134
## iter 290 value 627.761696
## iter 300 value 626.665449
## iter 310 value 626.046237
## iter 320 value 625.757689
## iter 330 value 625.591667
## iter 340 value 625.471784
## iter 350 value 625.395708
## iter 360 value 625.357167
## iter 370 value 625.314095
## iter 380 value 625.285399
## iter 390 value 625.215867
## iter 400 value 625.200467
## iter 410 value 623.252363
## iter 420 value 617.421335
## iter 430 value 612.639363
## iter 440 value 611.078901
## iter 450 value 609.006449
## iter 460 value 608.418468
## iter 470 value 607.992970
## iter 480 value 607.767551
## iter 490 value 607.585468
## iter 500 value 607.483476
## final value 607.483476
## stopped after 500 iterations
## # weights: 181
## initial value 1304399.167845
## iter 10 value 1141.288055
## iter 20 value 831.013472
## iter 30 value 672.990045
## iter 40 value 550.573160
## iter 50 value 432.802596
## iter 60 value 371.654068
## iter 70 value 324.210511
## iter 80 value 279.891800
## iter 90 value 253.076729
## iter 100 value 233.777589
## iter 110 value 218.600899
## iter 120 value 207.641212
## iter 130 value 198.577162
## iter 140 value 188.303842
## iter 150 value 179.245015
## iter 160 value 174.077584
## iter 170 value 168.610556
## iter 180 value 162.243985
## iter 190 value 155.118536
## iter 200 value 149.944819
## iter 210 value 144.145353
## iter 220 value 137.860236
## iter 230 value 131.067389
## iter 240 value 124.962772
## iter 250 value 120.114394
## iter 260 value 115.821158
## iter 270 value 112.115071
## iter 280 value 109.166425
## iter 290 value 106.931589
## iter 300 value 104.130669
## iter 310 value 101.861303
## iter 320 value 100.814380
## iter 330 value 99.545426
## iter 340 value 98.023952
## iter 350 value 96.433503
## iter 360 value 95.712559
## iter 370 value 95.561295
## iter 380 value 95.496647
## iter 390 value 95.388732
## iter 400 value 95.254251
## iter 410 value 95.128698
## iter 420 value 94.967333
## iter 430 value 94.858961
## iter 440 value 94.675341
## iter 450 value 94.359018
## iter 460 value 93.091568
## iter 470 value 92.117462
## iter 480 value 91.815564
## iter 490 value 91.421153
## iter 500 value 91.081678
## final value 91.081678
## stopped after 500 iterations
## # weights: 241
## initial value 1494792.852562
## iter 10 value 1650.365895
## iter 20 value 881.202073
## iter 30 value 620.063194
## iter 40 value 443.105554
## iter 50 value 352.457176
## iter 60 value 307.924822
## iter 70 value 267.342908
## iter 80 value 240.187042
## iter 90 value 209.420846
## iter 100 value 189.291861
## iter 110 value 172.084488
## iter 120 value 159.109883
## iter 130 value 147.861887
## iter 140 value 138.022472
## iter 150 value 128.825125
## iter 160 value 122.719523
## iter 170 value 117.736931
## iter 180 value 113.139305
## iter 190 value 108.260288
## iter 200 value 102.292462
## iter 210 value 97.274966
## iter 220 value 93.474227
## iter 230 value 88.978878
## iter 240 value 84.267821
## iter 250 value 78.416314
## iter 260 value 73.580207
## iter 270 value 68.086465
## iter 280 value 64.327722
## iter 290 value 61.779981
## iter 300 value 59.467241
## iter 310 value 57.626092
## iter 320 value 55.925421
## iter 330 value 54.086083
## iter 340 value 52.295793
## iter 350 value 50.624234
## iter 360 value 48.828349
## iter 370 value 47.917478
## iter 380 value 47.130496
## iter 390 value 46.414324
## iter 400 value 45.767326
## iter 410 value 44.977099
## iter 420 value 44.021037
## iter 430 value 43.173394
## iter 440 value 41.905073
## iter 450 value 40.429950
## iter 460 value 39.064303
## iter 470 value 38.066126
## iter 480 value 37.133087
## iter 490 value 36.819597
## iter 500 value 36.730349
## final value 36.730349
## stopped after 500 iterations
## # weights: 25
## initial value 1536496.891690
## iter 10 value 21717.180299
## iter 20 value 16517.954314
## iter 30 value 14853.986863
## iter 40 value 9493.319339
## iter 50 value 3352.471783
## iter 60 value 2546.656437
## iter 70 value 2362.381514
## iter 80 value 2144.918770
## iter 90 value 1772.315155
## iter 100 value 1727.300659
## iter 110 value 1603.895751
## iter 120 value 1529.043363
## iter 130 value 1517.249470
## iter 140 value 1517.047736
## iter 140 value 1517.047735
## final value 1517.047735
## converged
##################################
# Reporting the apparent results
# for the NN model
##################################
NN_DALEX <- DALEX::explain(NN_Tune,
data = MD.Model.Predictors,
y = MD$LIFEXP,
verbose = FALSE,
label = "NN")
(NN_DALEX_Performance <- model_performance(NN_DALEX))## Measures for: regression
## mse : 3.792286
## rmse : 1.947379
## r2 : 0.9387186
## mad : 1.118942
##
## Residuals:
## 0% 10% 20% 30% 40% 50% 60%
## -6.7019930 -2.2284111 -1.3596353 -0.7496302 -0.2984808 0.0323945 0.3834313
## 70% 80% 90% 100%
## 0.8343248 1.5340057 2.1809442 6.4827220
(NN_DALEX_Diagnostics <- model_diagnostics(NN_DALEX))## GENDER CONTIN INFMOR PERCAP
## Male :139 Africa :89 Min. :0.3365 Min. :-1.4775
## Female:153 Asia :73 1st Qu.:1.7047 1st Qu.: 0.6183
## Europe :62 Median :2.6100 Median : 1.7960
## North America:31 Mean :2.5569 Mean : 1.7571
## Oceania :18 3rd Qu.:3.5025 3rd Qu.: 2.7938
## South America:19 Max. :4.4864 Max. : 4.7293
## CLTECH NCOMOR y y_hat
## Min. : 0.00 Min. :1.866 Min. :52.84 Min. :56.39
## 1st Qu.: 24.35 1st Qu.:3.944 1st Qu.:66.93 1st Qu.:66.84
## Median : 82.80 Median :4.717 Median :73.53 Median :73.71
## Mean : 64.60 Mean :4.652 Mean :72.47 Mean :72.46
## 3rd Qu.:100.00 3rd Qu.:5.347 3rd Qu.:78.54 3rd Qu.:78.31
## Max. :100.00 Max. :7.959 Max. :87.45 Max. :85.01
## residuals abs_residuals label ids
## Min. :-6.7020 Min. :0.006932 Length:292 Min. : 1.00
## 1st Qu.:-0.9466 1st Qu.:0.423443 Class :character 1st Qu.: 73.75
## Median : 0.0324 Median :1.118942 Mode :character Median :146.50
## Mean : 0.0151 Mean :1.437687 Mean :146.50
## 3rd Qu.: 1.2088 3rd Qu.:2.042302 3rd Qu.:219.25
## Max. : 6.4827 Max. :6.701993 Max. :292.00
plot(NN_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("RF: Observed and Predicted LIFEXP")NN_DALEX_VariableImportance <- model_parts(NN_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
plot(NN_DALEX_VariableImportance)##################################
# Reporting the cross-validation results
# for the NN model
##################################
NN_Tune## Neural Network
##
## 292 samples
## 6 predictor
##
## Pre-processing: centered (4), scaled (4), ignore (2)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 264, 263, 263, 262, 263, 264, ...
## Resampling results across tuning parameters:
##
## size decay RMSE Rsquared MAE
## 2 0e+00 2.913158 0.8500380 2.299048
## 2 1e-05 3.316750 0.7817528 2.514331
## 2 1e-04 3.312748 0.9255037 2.623476
## 2 1e-03 2.179988 0.9262000 1.649639
## 2 1e-01 2.070060 0.9321827 1.594778
## 5 0e+00 5.259886 0.8372621 2.275294
## 5 1e-05 3.641720 0.8067295 2.076761
## 5 1e-04 2.622448 0.8956273 1.750379
## 5 1e-03 2.288813 0.9148856 1.683198
## 5 1e-01 2.244542 0.9208400 1.660874
## 10 0e+00 4.090892 0.8342633 2.272149
## 10 1e-05 6.251900 0.6529587 3.162672
## 10 1e-04 3.404514 0.8251865 2.140352
## 10 1e-03 2.828581 0.8793067 2.143924
## 10 1e-01 2.323915 0.9157548 1.729951
## 15 0e+00 4.182909 0.7642496 2.918018
## 15 1e-05 4.129136 0.7596288 2.961311
## 15 1e-04 4.024271 0.7862760 2.876569
## 15 1e-03 3.610424 0.8251640 2.696243
## 15 1e-01 2.543247 0.9030396 1.909596
## 20 0e+00 4.804438 0.7226852 3.660531
## 20 1e-05 4.303982 0.7508849 3.181808
## 20 1e-04 4.909855 0.6993867 3.617313
## 20 1e-03 4.837680 0.6951049 3.487130
## 20 1e-01 2.860518 0.8768559 2.040583
##
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were size = 2 and decay = 0.1.
NN_Tune$finalModel## a 10-2-1 network with 25 weights
## inputs: GENDERFemale CONTINAsia CONTINEurope CONTINNorth America CONTINOceania CONTINSouth America INFMOR PERCAP CLTECH NCOMOR
## output(s): .outcome
## options were - linear output units decay=0.1
(NN_Tune_RMSE <- NN_Tune$results[NN_Tune$results$size==NN_Tune$bestTune$size &
NN_Tune$results$decay==NN_Tune$bestTune$decay,
c("RMSE")])## [1] 2.07006
(NN_Tune_Rsquared <- NN_Tune$results[NN_Tune$results$size==NN_Tune$bestTune$size &
NN_Tune$results$decay==NN_Tune$bestTune$decay,
c("Rsquared")])## [1] 0.9321827
(NN_Tune_MAE <- NN_Tune$results[NN_Tune$results$size==NN_Tune$bestTune$size &
NN_Tune$results$decay==NN_Tune$bestTune$decay,
c("MAE")])## [1] 1.594778
##################################
# Defining the model hyperparameter values
# for the PLS model
##################################
PLS_Grid = expand.grid(ncomp = 1:5)
##################################
# Running the PLS model
# by setting the caret method to 'pls'
##################################
set.seed(12345678)
PLS_Tune <- train(x = MD.Model.Predictors,
y = MD$LIFEXP,
method = "pls",
tuneGrid = PLS_Grid,
trControl = KFold_Control)
##################################
# Reporting the apparent results
# for the PLS model
##################################
PLS_DALEX <- DALEX::explain(PLS_Tune,
data = MD.Model.Predictors,
y = MD$LIFEXP,
verbose = FALSE,
label = "PLS")
(PLS_DALEX_Performance <- model_performance(PLS_DALEX))## Measures for: regression
## mse : 5.876756
## rmse : 2.424202
## r2 : 0.9050347
## mad : 1.524704
##
## Residuals:
## 0% 10% 20% 30% 40% 50% 60%
## -7.8171653 -3.1634756 -1.8497887 -1.1383462 -0.5658555 0.2131137 0.6858102
## 70% 80% 90% 100%
## 1.2703500 1.8554207 2.8334884 6.6842346
(PLS_DALEX_Diagnostics <- model_diagnostics(PLS_DALEX))## GENDER CONTIN INFMOR PERCAP
## Male :139 Africa :89 Min. :0.3365 Min. :-1.4775
## Female:153 Asia :73 1st Qu.:1.7047 1st Qu.: 0.6183
## Europe :62 Median :2.6100 Median : 1.7960
## North America:31 Mean :2.5569 Mean : 1.7571
## Oceania :18 3rd Qu.:3.5025 3rd Qu.: 2.7938
## South America:19 Max. :4.4864 Max. : 4.7293
## CLTECH NCOMOR y y_hat
## Min. : 0.00 Min. :1.866 Min. :52.84 Min. :57.87
## 1st Qu.: 24.35 1st Qu.:3.944 1st Qu.:66.93 1st Qu.:66.38
## Median : 82.80 Median :4.717 Median :73.53 Median :73.39
## Mean : 64.60 Mean :4.652 Mean :72.47 Mean :72.47
## 3rd Qu.:100.00 3rd Qu.:5.347 3rd Qu.:78.54 3rd Qu.:78.18
## Max. :100.00 Max. :7.959 Max. :87.45 Max. :88.65
## residuals abs_residuals label ids
## Min. :-7.8172 Min. :0.01345 Length:292 Min. : 1.00
## 1st Qu.:-1.3989 1st Qu.:0.74951 Class :character 1st Qu.: 73.75
## Median : 0.2131 Median :1.52470 Mode :character Median :146.50
## Mean : 0.0000 Mean :1.89719 Mean :146.50
## 3rd Qu.: 1.5808 3rd Qu.:2.60138 3rd Qu.:219.25
## Max. : 6.6842 Max. :7.81717 Max. :292.00
plot(PLS_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("RF: Observed and Predicted LIFEXP")PLS_DALEX_VariableImportance <- model_parts(PLS_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
plot(PLS_DALEX_VariableImportance)##################################
# Reporting the cross-validation results
# for the PLS model
##################################
PLS_Tune## Partial Least Squares
##
## 292 samples
## 6 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 264, 263, 263, 262, 263, 264, ...
## Resampling results across tuning parameters:
##
## ncomp RMSE Rsquared MAE
## 1 4.997627 0.6039633 4.073632
## 2 3.251276 0.8416354 2.478243
## 3 2.705139 0.8916091 2.067882
## 4 2.572417 0.9014321 2.006329
## 5 2.463222 0.9087387 1.954148
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was ncomp = 5.
PLS_Tune$finalModel## Partial least squares regression , fitted with the orthogonal scores algorithm.
## Call:
## plsr(formula = .outcome ~ ., ncomp = ncomp, data = dat, method = "oscorespls")
(PLS_Tune_RMSE <- PLS_Tune$results[PLS_Tune$results$ncomp==PLS_Tune$bestTune$ncomp,
c("RMSE")])## [1] 2.463222
(PLS_Tune_Rsquared <- PLS_Tune$results[PLS_Tune$results$ncomp==PLS_Tune$bestTune$ncomp,
c("Rsquared")])## [1] 0.9087387
(PLS_Tune_MAE <- PLS_Tune$results[PLS_Tune$results$ncomp==PLS_Tune$bestTune$ncomp,
c("MAE")])## [1] 1.954148
##################################
# Defining the model hyperparameter values
# for the CUBIST model
##################################
CUBIST_Grid = expand.grid(committees = c(10, 20, 30, 40, 50),
neighbors = c(0, 3, 6, 9))
##################################
# Running the CUBIST model
# by setting the caret method to 'cubist'
##################################
set.seed(12345678)
CUBIST_Tune <- train(x = MD.Model.Predictors,
y = MD$LIFEXP,
method = "cubist",
tuneGrid = CUBIST_Grid,
trControl = KFold_Control)
##################################
# Reporting the apparent results
# for the CUBIST model
##################################
CUBIST_DALEX <- DALEX::explain(CUBIST_Tune,
data = MD.Model.Predictors,
y = MD$LIFEXP,
verbose = FALSE,
label = "CUBIST")
(CUBIST_DALEX_Performance <- model_performance(CUBIST_DALEX))## Measures for: regression
## mse : 3.658042
## rmse : 1.912601
## r2 : 0.9408879
## mad : 1.05533
##
## Residuals:
## 0% 10% 20% 30% 40% 50%
## -6.90777771 -2.15534625 -1.41800017 -0.72510388 -0.33359399 0.01040643
## 60% 70% 80% 90% 100%
## 0.32413965 0.72477280 1.37141615 2.49934372 6.82557834
(CUBIST_DALEX_Diagnostics <- model_diagnostics(CUBIST_DALEX))## GENDER CONTIN INFMOR PERCAP
## Male :139 Africa :89 Min. :0.3365 Min. :-1.4775
## Female:153 Asia :73 1st Qu.:1.7047 1st Qu.: 0.6183
## Europe :62 Median :2.6100 Median : 1.7960
## North America:31 Mean :2.5569 Mean : 1.7571
## Oceania :18 3rd Qu.:3.5025 3rd Qu.: 2.7938
## South America:19 Max. :4.4864 Max. : 4.7293
## CLTECH NCOMOR y y_hat
## Min. : 0.00 Min. :1.866 Min. :52.84 Min. :55.50
## 1st Qu.: 24.35 1st Qu.:3.944 1st Qu.:66.93 1st Qu.:66.51
## Median : 82.80 Median :4.717 Median :73.53 Median :73.61
## Mean : 64.60 Mean :4.652 Mean :72.47 Mean :72.40
## 3rd Qu.:100.00 3rd Qu.:5.347 3rd Qu.:78.54 3rd Qu.:78.06
## Max. :100.00 Max. :7.959 Max. :87.45 Max. :86.25
## residuals abs_residuals label ids
## Min. :-6.90778 Min. :0.003691 Length:292 Min. : 1.00
## 1st Qu.:-1.06822 1st Qu.:0.447052 Class :character 1st Qu.: 73.75
## Median : 0.01041 Median :1.055330 Mode :character Median :146.50
## Mean : 0.07148 Mean :1.409394 Mean :146.50
## 3rd Qu.: 1.00547 3rd Qu.:2.063695 3rd Qu.:219.25
## Max. : 6.82558 Max. :6.907778 Max. :292.00
plot(CUBIST_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("RF: Observed and Predicted LIFEXP")CUBIST_DALEX_VariableImportance <- model_parts(CUBIST_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
plot(CUBIST_DALEX_VariableImportance)##################################
# Reporting the cross-validation results
# for the CUBIST model
##################################
CUBIST_Tune## Cubist
##
## 292 samples
## 6 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 264, 263, 263, 262, 263, 264, ...
## Resampling results across tuning parameters:
##
## committees neighbors RMSE Rsquared MAE
## 10 0 2.143307 0.9284329 1.597946
## 10 3 2.161575 0.9264713 1.628394
## 10 6 2.135997 0.9293777 1.600862
## 10 9 2.120955 0.9307087 1.593085
## 20 0 2.118571 0.9293801 1.589678
## 20 3 2.171113 0.9255303 1.629055
## 20 6 2.138896 0.9287024 1.597065
## 20 9 2.118487 0.9303570 1.588802
## 30 0 2.104752 0.9307090 1.577201
## 30 3 2.171787 0.9257896 1.634946
## 30 6 2.135237 0.9293559 1.598399
## 30 9 2.113010 0.9311860 1.587534
## 40 0 2.099914 0.9312085 1.569779
## 40 3 2.164063 0.9263082 1.632470
## 40 6 2.126530 0.9299765 1.592498
## 40 9 2.104461 0.9318479 1.580217
## 50 0 2.096744 0.9316609 1.568414
## 50 3 2.158509 0.9267267 1.629668
## 50 6 2.120160 0.9304991 1.588673
## 50 9 2.097972 0.9324203 1.575005
##
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were committees = 50 and neighbors = 0.
CUBIST_Tune$finalModel##
## Call:
## cubist.default(x = x, y = y, committees = param$committees)
##
## Number of samples: 292
## Number of predictors: 6
##
## Number of committees: 50
## Number of rules per committee: 6, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2 ...
(CUBIST_Tune_RMSE <- CUBIST_Tune$results[CUBIST_Tune$results$committees==CUBIST_Tune$bestTune$committees &
CUBIST_Tune$results$neighbors==CUBIST_Tune$bestTune$neighbors,
c("RMSE")])## [1] 2.096744
(CUBIST_Tune_Rsquared <- CUBIST_Tune$results[CUBIST_Tune$results$committees==CUBIST_Tune$bestTune$committees &
CUBIST_Tune$results$neighbors==CUBIST_Tune$bestTune$neighbors,
c("Rsquared")])## [1] 0.9316609
(CUBIST_Tune_MAE <- CUBIST_Tune$results[CUBIST_Tune$results$committees==CUBIST_Tune$bestTune$committees &
CUBIST_Tune$results$neighbors==CUBIST_Tune$bestTune$neighbors,
c("MAE")])## [1] 1.568414
##################################
# Evaluating the models
# on the model test data
##################################
##################################
# Formulating the DALEX object
# for the Best GBM model
# as applied to the model test data
##################################
GBM_DALEX <- DALEX::explain(GBM_Tune,
data = MT.Model.Predictors,
y = MT$LIFEXP,
verbose = FALSE,
label = "GBM")
(GBM_DALEX_Performance <- model_performance(GBM_DALEX))## Measures for: regression
## mse : 4.877673
## rmse : 2.208545
## r2 : 0.9084603
## mad : 1.155262
##
## Residuals:
## 0% 10% 20% 30% 40% 50%
## -4.35206346 -2.32130175 -1.24214793 -0.82877725 -0.46233520 0.06134295
## 60% 70% 80% 90% 100%
## 0.59630498 0.73104236 1.75812056 3.45145715 6.41897554
(GBM_DALEX_Diagnostics <- model_diagnostics(GBM_DALEX))## GENDER CONTIN INFMOR PERCAP
## Male :43 Africa :17 Min. :0.6419 Min. :-1.4775
## Female:29 Asia :21 1st Qu.:1.7090 1st Qu.: 0.7926
## Europe :16 Median :2.6602 Median : 1.7266
## North America:11 Mean :2.5559 Mean : 1.7439
## Oceania : 2 3rd Qu.:3.4135 3rd Qu.: 2.8314
## South America: 5 Max. :4.3858 Max. : 4.4466
## CLTECH NCOMOR y y_hat
## Min. : 0.20 Min. :2.511 Min. :51.20 Min. :55.11
## 1st Qu.: 49.02 1st Qu.:3.887 1st Qu.:67.42 1st Qu.:68.18
## Median : 90.20 Median :4.742 Median :73.51 Median :73.02
## Mean : 72.08 Mean :4.752 Mean :72.61 Mean :72.33
## 3rd Qu.:100.00 3rd Qu.:5.631 3rd Qu.:78.47 3rd Qu.:78.53
## Max. :100.00 Max. :7.406 Max. :86.20 Max. :84.82
## residuals abs_residuals label ids
## Min. :-4.35206 Min. :0.003661 Length:72 Min. : 1.00
## 1st Qu.:-1.04139 1st Qu.:0.609636 Class :character 1st Qu.:18.75
## Median : 0.06134 Median :1.155262 Mode :character Median :36.50
## Mean : 0.27559 Mean :1.661957 Mean :36.50
## 3rd Qu.: 1.45652 3rd Qu.:2.352673 3rd Qu.:54.25
## Max. : 6.41898 Max. :6.418976 Max. :72.00
plot(GBM_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("GBM: Observed and Predicted LIFEXP")##################################
# Formulating the DALEX object
# for the Best RF model
# as applied to the model test data
##################################
RF_DALEX <- DALEX::explain(RF_Tune,
data = MT.Model.Predictors,
y = MT$LIFEXP,
verbose = FALSE,
label = "RF")
(RF_DALEX_Performance <- model_performance(RF_DALEX))## Measures for: regression
## mse : 6.25631
## rmse : 2.501262
## r2 : 0.8825872
## mad : 1.594092
##
## Residuals:
## 0% 10% 20% 30% 40% 50% 60%
## -7.2080243 -3.0176466 -2.1047296 -1.2247647 -0.2712163 0.1992440 0.7128073
## 70% 80% 90% 100%
## 1.1067986 1.7677385 3.1744799 7.4973476
(RF_DALEX_Diagnostics <- model_diagnostics(RF_DALEX))## GENDER CONTIN INFMOR PERCAP
## Male :43 Africa :17 Min. :0.6419 Min. :-1.4775
## Female:29 Asia :21 1st Qu.:1.7090 1st Qu.: 0.7926
## Europe :16 Median :2.6602 Median : 1.7266
## North America:11 Mean :2.5559 Mean : 1.7439
## Oceania : 2 3rd Qu.:3.4135 3rd Qu.: 2.8314
## South America: 5 Max. :4.3858 Max. : 4.4466
## CLTECH NCOMOR y y_hat
## Min. : 0.20 Min. :2.511 Min. :51.20 Min. :54.85
## 1st Qu.: 49.02 1st Qu.:3.887 1st Qu.:67.42 1st Qu.:68.50
## Median : 90.20 Median :4.742 Median :73.51 Median :73.19
## Mean : 72.08 Mean :4.752 Mean :72.61 Mean :72.58
## 3rd Qu.:100.00 3rd Qu.:5.631 3rd Qu.:78.47 3rd Qu.:78.61
## Max. :100.00 Max. :7.406 Max. :86.20 Max. :84.60
## residuals abs_residuals label ids
## Min. :-7.20802 Min. :0.01395 Length:72 Min. : 1.00
## 1st Qu.:-1.69056 1st Qu.:0.73760 Class :character 1st Qu.:18.75
## Median : 0.19924 Median :1.59409 Mode :character Median :36.50
## Mean : 0.02669 Mean :1.91950 Mean :36.50
## 3rd Qu.: 1.50013 3rd Qu.:2.74804 3rd Qu.:54.25
## Max. : 7.49735 Max. :7.49735 Max. :72.00
plot(RF_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("RF: Observed and Predicted LIFEXP")##################################
# Formulating the DALEX object
# for the Best NN model
# as applied to the model test data
##################################
NN_DALEX <- DALEX::explain(NN_Tune,
data = MT.Model.Predictors,
y = MT$LIFEXP,
verbose = FALSE,
label = "NN")
(NN_DALEX_Performance <- model_performance(NN_DALEX))## Measures for: regression
## mse : 5.300062
## rmse : 2.302186
## r2 : 0.9005332
## mad : 1.293056
##
## Residuals:
## 0% 10% 20% 30% 40% 50% 60%
## -6.0612264 -2.8063521 -1.3495125 -1.0121289 -0.3547678 0.2159281 0.6061218
## 70% 80% 90% 100%
## 1.1206919 1.8356939 2.8346540 7.8217567
(NN_DALEX_Diagnostics <- model_diagnostics(NN_DALEX))## GENDER CONTIN INFMOR PERCAP
## Male :43 Africa :17 Min. :0.6419 Min. :-1.4775
## Female:29 Asia :21 1st Qu.:1.7090 1st Qu.: 0.7926
## Europe :16 Median :2.6602 Median : 1.7266
## North America:11 Mean :2.5559 Mean : 1.7439
## Oceania : 2 3rd Qu.:3.4135 3rd Qu.: 2.8314
## South America: 5 Max. :4.3858 Max. : 4.4466
## CLTECH NCOMOR y y_hat
## Min. : 0.20 Min. :2.511 Min. :51.20 Min. :52.40
## 1st Qu.: 49.02 1st Qu.:3.887 1st Qu.:67.42 1st Qu.:66.97
## Median : 90.20 Median :4.742 Median :73.51 Median :73.22
## Mean : 72.08 Mean :4.752 Mean :72.61 Mean :72.48
## 3rd Qu.:100.00 3rd Qu.:5.631 3rd Qu.:78.47 3rd Qu.:78.83
## Max. :100.00 Max. :7.406 Max. :86.20 Max. :84.49
## residuals abs_residuals label ids
## Min. :-6.0612 Min. :0.06061 Length:72 Min. : 1.00
## 1st Qu.:-1.2126 1st Qu.:0.72930 Class :character 1st Qu.:18.75
## Median : 0.2159 Median :1.29306 Mode :character Median :36.50
## Mean : 0.1293 Mean :1.77153 Mean :36.50
## 3rd Qu.: 1.5601 3rd Qu.:2.67075 3rd Qu.:54.25
## Max. : 7.8218 Max. :7.82176 Max. :72.00
plot(NN_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("NN: Observed and Predicted LIFEXP")##################################
# Formulating the DALEX object
# for the Best PLS model
# as applied to the model test data
##################################
PLS_DALEX <- DALEX::explain(PLS_Tune,
data = MT.Model.Predictors,
y = MT$LIFEXP,
verbose = FALSE,
label = "PLS")
(PLS_DALEX_Performance <- model_performance(PLS_DALEX))## Measures for: regression
## mse : 7.220162
## rmse : 2.687036
## r2 : 0.8644985
## mad : 1.875765
##
## Residuals:
## 0% 10% 20% 30% 40% 50% 60%
## -6.7868694 -2.5089071 -1.8150301 -1.1801931 -0.4920039 0.1375484 0.7844976
## 70% 80% 90% 100%
## 2.0494752 2.6262401 3.8453344 6.0828174
(PLS_DALEX_Diagnostics <- model_diagnostics(PLS_DALEX))## GENDER CONTIN INFMOR PERCAP
## Male :43 Africa :17 Min. :0.6419 Min. :-1.4775
## Female:29 Asia :21 1st Qu.:1.7090 1st Qu.: 0.7926
## Europe :16 Median :2.6602 Median : 1.7266
## North America:11 Mean :2.5559 Mean : 1.7439
## Oceania : 2 3rd Qu.:3.4135 3rd Qu.: 2.8314
## South America: 5 Max. :4.3858 Max. : 4.4466
## CLTECH NCOMOR y y_hat
## Min. : 0.20 Min. :2.511 Min. :51.20 Min. :57.14
## 1st Qu.: 49.02 1st Qu.:3.887 1st Qu.:67.42 1st Qu.:67.18
## Median : 90.20 Median :4.742 Median :73.51 Median :72.99
## Mean : 72.08 Mean :4.752 Mean :72.61 Mean :72.33
## 3rd Qu.:100.00 3rd Qu.:5.631 3rd Qu.:78.47 3rd Qu.:77.39
## Max. :100.00 Max. :7.406 Max. :86.20 Max. :84.75
## residuals abs_residuals label ids
## Min. :-6.7869 Min. :0.04887 Length:72 Min. : 1.00
## 1st Qu.:-1.4065 1st Qu.:0.82974 Class :character 1st Qu.:18.75
## Median : 0.1375 Median :1.87576 Mode :character Median :36.50
## Mean : 0.2816 Mean :2.15539 Mean :36.50
## 3rd Qu.: 2.4014 3rd Qu.:3.14434 3rd Qu.:54.25
## Max. : 6.0828 Max. :6.78687 Max. :72.00
plot(PLS_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("PLS: Observed and Predicted LIFEXP")##################################
# Formulating the DALEX object
# for the Best CUBIST model
# as applied to the model test data
##################################
CUBIST_DALEX <- DALEX::explain(CUBIST_Tune,
data = MT.Model.Predictors,
y = MT$LIFEXP,
verbose = FALSE,
label = "CUBIST")
(CUBIST_DALEX_Performance <- model_performance(CUBIST_DALEX))## Measures for: regression
## mse : 4.955851
## rmse : 2.226174
## r2 : 0.9069931
## mad : 1.555514
##
## Residuals:
## 0% 10% 20% 30% 40% 50%
## -6.27483923 -2.34803998 -1.75093724 -1.08073022 -0.25740164 0.08468332
## 60% 70% 80% 90% 100%
## 0.74473043 1.16073038 1.70618640 2.84412369 5.15525385
(CUBIST_DALEX_Diagnostics <- model_diagnostics(CUBIST_DALEX))## GENDER CONTIN INFMOR PERCAP
## Male :43 Africa :17 Min. :0.6419 Min. :-1.4775
## Female:29 Asia :21 1st Qu.:1.7090 1st Qu.: 0.7926
## Europe :16 Median :2.6602 Median : 1.7266
## North America:11 Mean :2.5559 Mean : 1.7439
## Oceania : 2 3rd Qu.:3.4135 3rd Qu.: 2.8314
## South America: 5 Max. :4.3858 Max. : 4.4466
## CLTECH NCOMOR y y_hat
## Min. : 0.20 Min. :2.511 Min. :51.20 Min. :55.56
## 1st Qu.: 49.02 1st Qu.:3.887 1st Qu.:67.42 1st Qu.:67.93
## Median : 90.20 Median :4.742 Median :73.51 Median :72.67
## Mean : 72.08 Mean :4.752 Mean :72.61 Mean :72.49
## 3rd Qu.:100.00 3rd Qu.:5.631 3rd Qu.:78.47 3rd Qu.:77.88
## Max. :100.00 Max. :7.406 Max. :86.20 Max. :84.71
## residuals abs_residuals label ids
## Min. :-6.27484 Min. :0.01256 Length:72 Min. : 1.00
## 1st Qu.:-1.54890 1st Qu.:0.71120 Class :character 1st Qu.:18.75
## Median : 0.08468 Median :1.55551 Mode :character Median :36.50
## Mean : 0.11581 Mean :1.73586 Mean :36.50
## 3rd Qu.: 1.40404 3rd Qu.:2.38064 3rd Qu.:54.25
## Max. : 5.15525 Max. :6.27484 Max. :72.00
plot(CUBIST_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("CUBIST: Observed and Predicted LIFEXP")##################################
# Consolidating the performance
# on the model test data
##################################
plot(GBM_DALEX_Performance,
RF_DALEX_Performance,
NN_DALEX_Performance,
PLS_DALEX_Performance,
CUBIST_DALEX_Performance)plot(GBM_DALEX_Performance,
RF_DALEX_Performance,
NN_DALEX_Performance,
PLS_DALEX_Performance,
CUBIST_DALEX_Performance,
geom = "boxplot")plot(GBM_DALEX_Performance,
RF_DALEX_Performance,
NN_DALEX_Performance,
PLS_DALEX_Performance,
CUBIST_DALEX_Performance,
geom = "histogram")##################################
# Consolidating the variable importance
# on the model test data
##################################
GBM_DALEX_VariableImportance <- model_parts(GBM_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
RF_DALEX_VariableImportance <- model_parts(RF_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
NN_DALEX_VariableImportance <- model_parts(NN_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
PLS_DALEX_VariableImportance <- model_parts(PLS_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
CUBIST_DALEX_VariableImportance <- model_parts(CUBIST_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
plot(GBM_DALEX_VariableImportance,
RF_DALEX_VariableImportance,
NN_DALEX_VariableImportance,
PLS_DALEX_VariableImportance,
CUBIST_DALEX_VariableImportance)##################################
# Summarizing the variable importance
# for the final model - GBM
##################################
GBM_DALEX_VariableImportance## variable mean_dropout_loss label
## 1 _full_model_ 2.208545 GBM
## 2 PERCAP 2.373317 GBM
## 3 CLTECH 2.479101 GBM
## 4 GENDER 2.625180 GBM
## 5 CONTIN 2.633023 GBM
## 6 NCOMOR 3.995838 GBM
## 7 INFMOR 7.080997 GBM
## 8 _baseline_ 10.054004 GBM
plot(GBM_DALEX_VariableImportance)##################################
# Formulating the partial dependence plots
# for the final model - GBM
# using the numeric variables
##################################
GBM_DALEX_PartialDependencePlot_INFMOR <- model_profile(GBM_DALEX,
variables = "INFMOR")
GBM_DALEX_PartialDependencePlot_NCOMOR <- model_profile(GBM_DALEX,
variables = "NCOMOR")
GBM_DALEX_PartialDependencePlot_CLTECH <- model_profile(GBM_DALEX,
variables = "CLTECH")
GBM_DALEX_PartialDependencePlot_PERCAP <- model_profile(GBM_DALEX,
variables = "PERCAP")
(GBM_DALEX_PDP_INFMOR <- plot(GBM_DALEX_PartialDependencePlot_INFMOR,
geom = "profiles"))(GBM_DALEX_PDP_NCOMOR <- plot(GBM_DALEX_PartialDependencePlot_NCOMOR,
geom = "profiles"))(GBM_DALEX_PDP_CLTECH <- plot(GBM_DALEX_PartialDependencePlot_CLTECH,
geom = "profiles"))(GBM_DALEX_PDP_PERCAP <- plot(GBM_DALEX_PartialDependencePlot_PERCAP,
geom = "profiles"))##################################
# Formulating the grouped partial dependence plots
# for the final model - GBM
# using the numeric variables
# stratified by GENDER
##################################
GBM_DALEX_GroupedPartialDependencePlot_INFMOR <- model_profile(GBM_DALEX,
variables = "INFMOR",
groups = "GENDER")
GBM_DALEX_GroupedPartialDependencePlot_NCOMOR <- model_profile(GBM_DALEX,
variables = "NCOMOR",
groups = "GENDER")
GBM_DALEX_GroupedPartialDependencePlot_CLTECH <- model_profile(GBM_DALEX,
variables = "CLTECH",
groups = "GENDER")
GBM_DALEX_GroupedPartialDependencePlot_PERCAP <- model_profile(GBM_DALEX,
variables = "PERCAP",
groups = "GENDER")
(GBM_DALEX_GPDP_INFMOR <- plot(GBM_DALEX_GroupedPartialDependencePlot_INFMOR,
geom = "profiles"))(GBM_DALEX_GPDP_NCOMOR <- plot(GBM_DALEX_GroupedPartialDependencePlot_NCOMOR,
geom = "profiles"))(GBM_DALEX_GPDP_CLTECH <- plot(GBM_DALEX_GroupedPartialDependencePlot_CLTECH,
geom = "profiles"))(GBM_DALEX_GPDP_PERCAP <- plot(GBM_DALEX_GroupedPartialDependencePlot_PERCAP,
geom = "profiles"))##################################
# Formulating the grouped partial dependence plots
# for the final model - GBM
# using the numeric variables
# stratified by CONTIN
##################################
GBM_DALEX_GroupedPartialDependencePlot_INFMOR <- model_profile(GBM_DALEX,
variables = "INFMOR",
groups = "CONTIN")
GBM_DALEX_GroupedPartialDependencePlot_NCOMOR <- model_profile(GBM_DALEX,
variables = "NCOMOR",
groups = "CONTIN")
GBM_DALEX_GroupedPartialDependencePlot_CLTECH <- model_profile(GBM_DALEX,
variables = "CLTECH",
groups = "CONTIN")
GBM_DALEX_GroupedPartialDependencePlot_PERCAP <- model_profile(GBM_DALEX,
variables = "PERCAP",
groups = "CONTIN")
(GBM_DALEX_GPDP_INFMOR <- plot(GBM_DALEX_GroupedPartialDependencePlot_INFMOR,
geom = "profiles"))(GBM_DALEX_GPDP_NCOMOR <- plot(GBM_DALEX_GroupedPartialDependencePlot_NCOMOR,
geom = "profiles"))(GBM_DALEX_GPDP_CLTECH <- plot(GBM_DALEX_GroupedPartialDependencePlot_CLTECH,
geom = "profiles"))(GBM_DALEX_GPDP_PERCAP <- plot(GBM_DALEX_GroupedPartialDependencePlot_PERCAP,
geom = "profiles"))##################################
# Formulating the partial dependence plots
# for the final model - GBM
# using the factor variables
##################################
GBM_DALEX_PartialDependencePlot_GENDER <- model_profile(GBM_DALEX,
variable_type = 'categorical',
variables = "GENDER")
GBM_DALEX_PartialDependencePlot_CONTIN <- model_profile(GBM_DALEX,
variable_type = 'categorical',
variables = "CONTIN")
(GBM_DALEX_PDP_GENDER <- plot(GBM_DALEX_PartialDependencePlot_GENDER,
geom = "profiles"))(GBM_DALEX_PDP_CONTIN <- plot(GBM_DALEX_PartialDependencePlot_CONTIN,
geom = "profiles"))##################################
# Formulating the sampled instances
# for illustration
##################################
(Instance_1_Philippines_Female <- PME[PME$COUNTRY=="Philippines" & PME$GENDER=="Female",
c("GENDER","CONTIN","INFMOR","PERCAP","CLTECH","NCOMOR","LIFEXP")])## GENDER CONTIN INFMOR PERCAP CLTECH NCOMOR LIFEXP
## 141 Female Asia 2.944439 1.248566 47.4 4.704261 75.505
(Instance_2_Philippines_Male <- PME[PME$COUNTRY=="Philippines" & PME$GENDER=="Male",
c("GENDER","CONTIN","INFMOR","PERCAP","CLTECH","NCOMOR","LIFEXP")])## GENDER CONTIN INFMOR PERCAP CLTECH NCOMOR LIFEXP
## 338 Male Asia 3.173878 1.248566 47.4 5.950788 67.263
##################################
# Obtaining the breakdown plots
# for the individual instances
##################################
(Instance_1_GBM_BDP <- DALEX::predict_parts(explainer = GBM_DALEX,
new_observation = Instance_1_Philippines_Female[,c(1:6)],
type = "break_down"))## contribution
## GBM: intercept 72.334
## GBM: GENDER = Female 1.285
## GBM: CONTIN = Asia 0.525
## GBM: INFMOR = 2.944 0.695
## GBM: NCOMOR = 4.704 -0.161
## GBM: PERCAP = 1.249 -0.111
## GBM: CLTECH = 47.4 0.014
## GBM: prediction 74.581
plot(Instance_1_GBM_BDP)(Instance_2_GBM_BDP <- DALEX::predict_parts(explainer = GBM_DALEX,
new_observation = Instance_2_Philippines_Male[,c(1:6)],
type = "break_down"))## contribution
## GBM: intercept 72.334
## GBM: NCOMOR = 5.951 -3.063
## GBM: INFMOR = 3.174 -0.819
## GBM: GENDER = Male -0.926
## GBM: CONTIN = Asia 0.813
## GBM: PERCAP = 1.249 0.256
## GBM: CLTECH = 47.4 -0.142
## GBM: prediction 68.452
plot(Instance_2_GBM_BDP)##################################
# Obtaining the shapley additive explanations
# for the individual instances
##################################
(Instance_1_GBM_SHAP <- DALEX::predict_parts(explainer = GBM_DALEX,
new_observation = Instance_1_Philippines_Female[,c(1:6)],
type = "shap",
B = 25))## min q1 median mean q3
## GBM: CLTECH = 47.4 -0.1096761 0.01141644 0.0425315 0.04627025 0.11125913
## GBM: CONTIN = Asia 0.1997490 0.27790908 0.4226572 0.42047277 0.55714718
## GBM: GENDER = Female 1.0250692 1.17867184 1.2296506 1.22132808 1.28532575
## GBM: INFMOR = 2.944 0.5616631 0.60727978 1.2181237 1.12691237 1.49359879
## GBM: NCOMOR = 4.704 -0.9672737 -0.69107878 -0.5245096 -0.47182445 -0.17498794
## GBM: PERCAP = 1.249 -0.3942109 -0.20796143 -0.1052200 -0.09626333 -0.08089423
## max
## GBM: CLTECH = 47.4 0.1437923
## GBM: CONTIN = Asia 0.6872704
## GBM: GENDER = Female 1.3923546
## GBM: INFMOR = 2.944 1.6130390
## GBM: NCOMOR = 4.704 0.0998502
## GBM: PERCAP = 1.249 0.1876230
plot(Instance_1_GBM_SHAP)(Instance_2_GBM_SHAP <- DALEX::predict_parts(explainer = GBM_DALEX,
new_observation = Instance_2_Philippines_Male[,c(1:6)],
type = "shap",
B = 25))## min q1 median mean q3
## GBM: CLTECH = 47.4 -0.1833863 -0.13902978 -0.10967607 -0.08443553 -0.03754566
## GBM: CONTIN = Asia 0.4248417 0.61800333 0.62294753 0.63130944 0.66081998
## GBM: GENDER = Male -1.0374690 -0.89214274 -0.80993627 -0.79832321 -0.71019714
## GBM: INFMOR = 3.174 -2.0087792 -1.95450059 -1.74699387 -1.44666001 -0.81061732
## GBM: NCOMOR = 5.951 -3.1802712 -2.89954326 -1.90426390 -2.23039094 -1.80864334
## GBM: PERCAP = 1.249 -0.2740559 -0.08089423 -0.01069142 0.04676586 0.24048722
## max
## GBM: CLTECH = 47.4 0.06412474
## GBM: CONTIN = Asia 0.90402611
## GBM: GENDER = Male -0.61636661
## GBM: INFMOR = 3.174 -0.48666306
## GBM: NCOMOR = 5.951 -1.46373246
## GBM: PERCAP = 1.249 0.26382681
plot(Instance_2_GBM_SHAP)##################################
# Obtaining the ceteris paribus profiles
# for the individual instances
##################################
(Instance_1_GBM_CPP <- DALEX::predict_profile(explainer = GBM_DALEX,
new_observation = Instance_1_Philippines_Female[,c(1:6)]))## Top profiles :
## GENDER CONTIN INFMOR PERCAP CLTECH NCOMOR _yhat_ _vname_
## 141 Male Asia 2.944439 1.248566 47.4 4.704261 72.52199 GENDER
## 141.1 Female Asia 2.944439 1.248566 47.4 4.704261 74.58094 GENDER
## 1411 Female Africa 2.944439 1.248566 47.4 4.704261 73.53995 CONTIN
## 141.110 Female Asia 2.944439 1.248566 47.4 4.704261 74.58094 CONTIN
## 141.2 Female Europe 2.944439 1.248566 47.4 4.704261 74.92891 CONTIN
## 141.3 Female North America 2.944439 1.248566 47.4 4.704261 74.52190 CONTIN
## _ids_ _label_
## 141 141 GBM
## 141.1 141 GBM
## 1411 141 GBM
## 141.110 141 GBM
## 141.2 141 GBM
## 141.3 141 GBM
##
##
## Top observations:
## GENDER CONTIN INFMOR PERCAP CLTECH NCOMOR _yhat_ _label_ _ids_
## 141 Female Asia 2.944439 1.248566 47.4 4.704261 74.58094 GBM 1
plot(Instance_1_GBM_CPP,
variables = c("INFMOR","PERCAP","CLTECH","NCOMOR")) +
ggtitle("Ceteris-paribus profile", "") +
ylim(55, 80)plot(Instance_1_GBM_CPP,
variables = c("GENDER","CONTIN"),
variable_type = "categorical",
categorical_type = "bars") +
ggtitle("Ceteris-paribus profile", "")(Instance_2_GBM_CPP <- DALEX::predict_profile(explainer = GBM_DALEX,
new_observation = Instance_2_Philippines_Male[,c(1:6)]))## Top profiles :
## GENDER CONTIN INFMOR PERCAP CLTECH NCOMOR _yhat_ _vname_
## 338 Male Asia 3.173878 1.248566 47.4 5.950788 68.45231 GENDER
## 338.1 Female Asia 3.173878 1.248566 47.4 5.950788 70.66729 GENDER
## 3381 Male Africa 3.173878 1.248566 47.4 5.950788 66.21920 CONTIN
## 338.110 Male Asia 3.173878 1.248566 47.4 5.950788 68.45231 CONTIN
## 338.2 Male Europe 3.173878 1.248566 47.4 5.950788 68.11751 CONTIN
## 338.3 Male North America 3.173878 1.248566 47.4 5.950788 68.25643 CONTIN
## _ids_ _label_
## 338 338 GBM
## 338.1 338 GBM
## 3381 338 GBM
## 338.110 338 GBM
## 338.2 338 GBM
## 338.3 338 GBM
##
##
## Top observations:
## GENDER CONTIN INFMOR PERCAP CLTECH NCOMOR _yhat_ _label_ _ids_
## 338 Male Asia 3.173878 1.248566 47.4 5.950788 68.45231 GBM 1
plot(Instance_2_GBM_CPP,
variables = c("INFMOR","PERCAP","CLTECH","NCOMOR")) +
ggtitle("Ceteris-paribus profile", "") +
ylim(55, 80)plot(Instance_2_GBM_CPP,
variables = c("GENDER","CONTIN"),
variable_type = "categorical",
categorical_type = "bars") +
ggtitle("Ceteris-paribus profile", "")Instance_1_GBM_LFP <- predict_diagnostics(explainer = GBM_DALEX,
new_observation = Instance_1_Philippines_Female[,c(1:6)],
neighbours = 50)
plot(Instance_1_GBM_LFP)Instance_2_GBM_LFP <- predict_diagnostics(explainer = GBM_DALEX,
new_observation = Instance_2_Philippines_Male[,c(1:6)],
neighbours = 50)
plot(Instance_2_GBM_LFP)Instance_1_GBM_LFP <- predict_diagnostics(explainer = GBM_DALEX,
new_observation = Instance_1_Philippines_Female[,c(1:6)],
neighbours = 5,
variables = c("INFMOR","NCOMOR","CLTECH","PERCAP"))
plot(Instance_1_GBM_LFP)Instance_1_GBM_LFP <- predict_diagnostics(explainer = GBM_DALEX,
new_observation = Instance_1_Philippines_Female[,c(1:6)],
neighbours = 5,
variables = c("GENDER","CONTIN"))
plot(Instance_1_GBM_LFP)Instance_2_GBM_LFP <- predict_diagnostics(explainer = GBM_DALEX,
new_observation = Instance_2_Philippines_Male[,c(1:6)],
neighbours = 5,
variables = c("INFMOR","NCOMOR","CLTECH","PERCAP"))
plot(Instance_2_GBM_LFP)Instance_2_GBM_LFP <- predict_diagnostics(explainer = GBM_DALEX,
new_observation = Instance_2_Philippines_Male[,c(1:6)],
neighbours = 5,
variables = c("GENDER","CONTIN"))
plot(Instance_2_GBM_LFP)